Information theory and the ethylene genetic network
González-García, José S
2011-01-01
The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of
Information theory and the ethylene genetic network.
González-García, José S; Díaz, José
2011-10-01
The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of
Recent developments in quantitative graph theory: information inequalities for networks.
Dehmer, Matthias; Sivakumar, Lavanya
2012-01-01
In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs.
Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
Dehmer, Matthias; Sivakumar, Lavanya
2012-01-01
In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs. PMID:22355362
Methods of information theory and algorithmic complexity for network biology.
Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper
2016-03-01
We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity. Copyright © 2016 Elsevier Ltd. All rights reserved.
2003-04-01
resources. This idea can be extended to optimize or prevent adverse effects from critical resources in addition to band- width and CPU. Memory , time of...tolerance. Active networks form an ideal environment in which to study the effects of trade-offs in algorithmic and static information representation...39 The effect of a partition on MML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Symbol
Information theory in systems biology. Part I: Gene regulatory and metabolic networks.
Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali
2016-03-01
"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.
Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H
2014-11-01
Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design. Copyright © 2014 Elsevier Ltd. All rights reserved.
Information theory in systems biology. Part II: protein-protein interaction and signaling networks.
Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali
2016-03-01
By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.
The use of network theory to model disparate ship design information
NASA Astrophysics Data System (ADS)
Rigterink, Douglas; Piks, Rebecca; Singer, David J.
2014-06-01
This paper introduces the use of network theory to model and analyze disparate ship design information. This work will focus on a ship's distributed systems and their intra- and intersystem structures and interactions. The three system to be analyzed are: a passageway system, an electrical system, and a fire fighting system. These systems will be analyzed individually using common network metrics to glean information regarding their structures and attributes. The systems will also be subjected to community detection algorithms both separately and as a multiplex network to compare their similarities, differences, and interactions. Network theory will be shown to be useful in the early design stage due to its simplicity and ability to model any shipboard system.
Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling
Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.
2011-01-01
Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571
Protein signaling networks from single cell fluctuations and information theory profiling.
Shin, Young Shik; Remacle, F; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R D; Heath, James R
2011-05-18
Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network.
Evaluation of neural network robust reliability using information-gap theory.
Pierce, S Gareth; Ben-Haim, Yakov; Worden, Keith; Manson, Graeme
2006-11-01
A novel technique for the evaluation of neural network robustness against uncertainty using a nonprobabilistic approach is presented. Conventional optimization techniques were employed to train multilayer perceptron (MLP) networks, which were then probed with an uncertainty analysis using an information-gap model to quantify the network response to uncertainty in the input data. It is demonstrated that the best performing network on data with low uncertainty is not in general the optimal network on data with a higher degree of input uncertainty. Using the concepts of information-gap theory, this paper develops a theoretical framework for information-gap uncertainty applied to neural networks, and explores the practical application of the procedure to three sample cases. The first consists of a simple two-dimensional (2-D) classification network operating on a known Gaussian distribution, the second a nine-lass vibration classification problem from an aircraft wing, and the third a two-class example from a database of breast cancer incidence.
Yoon, Hong-Jun; Tourassi, Georgia
2014-01-01
Analyzing the contents of online social networks is an effective process for monitoring and understanding peoples behaviors. Since the nature of conversation and information propagation is similar to traditional conversation and learning, one of the popular socio-cognitive methods, social cognitive theory was applied to online social networks to. Two major news topics about colon cancer were chosen to monitor traffic of Twitter messages. The activity of leaders on the issue (i.e., news companies or people will prior Twitter activity on topics related to colon cancer) was monitored. In addition, the activity of followers , people who never discussed the topics before, but replied to the discussions was also monitored. Topics that produce tangible benefits such as positive outcomes from appropriate preventive actions received dramatically more attention and online social media traffic. Such characteristics can be explained with social cognitive theory and thus present opportunities for effective health campaigns.
Yoon, Hong-Jun; Tourassi, Georgia
2015-01-01
Analyzing the contents of online social networks is an effective process for monitoring and understanding peoples’ behaviors. Since the nature of conversation and information propagation is similar to traditional conversation and learning, one of the popular socio-cognitive methods, social cognitive theory was applied to online social networks to. Two major news topics about colon cancer were chosen to monitor traffic of Twitter messages. The activity of “leaders” on the issue (i.e., news companies or people will prior Twitter activity on topics related to colon cancer) was monitored. In addition, the activity of “followers”, people who never discussed the topics before, but replied to the discussions was also monitored. Topics that produce tangible benefits such as positive outcomes from appropriate preventive actions received dramatically more attention and online social media traffic. Such characteristics can be explained with social cognitive theory and thus present opportunities for effective health campaigns. PMID:25973446
Yoon, Hong-Jun; Tourassi, Georgia
2014-05-01
Analyzing the contents of online social networks is an effective process for monitoring and understanding peoples' behaviors. Since the nature of conversation and information propagation is similar to traditional conversation and learning, one of the popular socio-cognitive methods, social cognitive theory was applied to online social networks to. Two major news topics about colon cancer were chosen to monitor traffic of Twitter messages. The activity of "leaders" on the issue (i.e., news companies or people will prior Twitter activity on topics related to colon cancer) was monitored. In addition, the activity of "followers", people who never discussed the topics before, but replied to the discussions was also monitored. Topics that produce tangible benefits such as positive outcomes from appropriate preventive actions received dramatically more attention and online social media traffic. Such characteristics can be explained with social cognitive theory and thus present opportunities for effective health campaigns.
NASA Astrophysics Data System (ADS)
Alfonso, Leonardo; Chacon, Juan; Solomatine, Dimitri
2016-04-01
The EC-FP7 WeSenseIt project proposes the development of a Citizen Observatory of Water, aiming at enhancing environmental monitoring and forecasting with the help of citizens equipped with low-cost sensors and personal devices such as smartphones and smart umbrellas. In this regard, Citizen Observatories may complement the limited data availability in terms of spatial and temporal density, which is of interest, among other areas, to improve hydraulic and hydrological models. At this point, the following question arises: how can citizens, who are part of a citizen observatory, be optimally guided so that the data they collect and send is useful to improve modelling and water management? This research proposes a new methodology to identify the optimal location and timing of potential observations coming from moving sensors of hydrological variables. The methodology is based on Information Theory, which has been widely used in hydrometric monitoring design [1-4]. In particular, the concepts of Joint Entropy, as a measure of the amount of information that is contained in a set of random variables, which, in our case, correspond to the time series of hydrological variables captured at given locations in a catchment. The methodology presented is a step forward in the state of the art because it solves the multiobjective optimisation problem of getting simultaneously the minimum number of informative and non-redundant sensors needed for a given time, so that the best configuration of monitoring sites is found at every particular moment in time. To this end, the existing algorithms have been improved to make them efficient. The method is applied to cases in The Netherlands, UK and Italy and proves to have a great potential to complement the existing in-situ monitoring networks. [1] Alfonso, L., A. Lobbrecht, and R. Price (2010a), Information theory-based approach for location of monitoring water level gauges in polders, Water Resour. Res., 46(3), W03528 [2] Alfonso, L., A
Optimization of hydrometric monitoring network in urban drainage systems using information theory.
Yazdi, J
2017-10-01
Regular and continuous monitoring of urban runoff in both quality and quantity aspects is of great importance for controlling and managing surface runoff. Due to the considerable costs of establishing new gauges, optimization of the monitoring network is essential. This research proposes an approach for site selection of new discharge stations in urban areas, based on entropy theory in conjunction with multi-objective optimization tools and numerical models. The modeling framework provides an optimal trade-off between the maximum possible information content and the minimum shared information among stations. This approach was applied to the main surface-water collection system in Tehran to determine new optimal monitoring points under the cost considerations. Experimental results on this drainage network show that the obtained cost-effective designs noticeably outperform the consulting engineers' proposal in terms of both information contents and shared information. The research also determined the highly frequent sites at the Pareto front which might be important for decision makers to give a priority for gauge installation on those locations of the network.
2013-01-01
Background Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. Results We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as “pathwise”. The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. Conclusions As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the
A new measure based on degree distribution that links information theory and network graph analysis
2012-01-01
Background Detailed connection maps of human and nonhuman brains are being generated with new technologies, and graph metrics have been instrumental in understanding the general organizational features of these structures. Neural networks appear to have small world properties: they have clustered regions, while maintaining integrative features such as short average pathlengths. Results We captured the structural characteristics of clustered networks with short average pathlengths through our own variable, System Difference (SD), which is computationally simple and calculable for larger graph systems. SD is a Jaccardian measure generated by averaging all of the differences in the connection patterns between any two nodes of a system. We calculated SD over large random samples of matrices and found that high SD matrices have a low average pathlength and a larger number of clustered structures. SD is a measure of degree distribution with high SD matrices maximizing entropic properties. Phi (Φ), an information theory metric that assesses a system’s capacity to integrate information, correlated well with SD - with SD explaining over 90% of the variance in systems above 11 nodes (tested for 4 to 13 nodes). However, newer versions of Φ do not correlate well with the SD metric. Conclusions The new network measure, SD, provides a link between high entropic structures and degree distributions as related to small world properties. PMID:22726594
Sayyed-Ahmad, Abdallah; Tuncay, Kagan; Ortoleva, Peter J
2007-01-01
Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the construction of the network of
ERIC Educational Resources Information Center
Heo, Gyeong Mi; Lee, Romee
2013-01-01
This paper uses an Activity Theory framework to explore adult user activities and informal learning processes as reflected in their blogs and social network sites (SNS). Using the assumption that a web-based space is an activity system in which learning occurs, typical features of the components were investigated and each activity system then…
2010-01-01
Background Actor-Network Theory (ANT) is an increasingly influential, but still deeply contested, approach to understand humans and their interactions with inanimate objects. We argue that health services research, and in particular evaluations of complex IT systems in health service organisations, may benefit from being informed by Actor-Network Theory perspectives. Discussion Despite some limitations, an Actor-Network Theory-based approach is conceptually useful in helping to appreciate the complexity of reality (including the complexity of organisations) and the active role of technology in this context. This can prove helpful in understanding how social effects are generated as a result of associations between different actors in a network. Of central importance in this respect is that Actor-Network Theory provides a lens through which to view the role of technology in shaping social processes. Attention to this shaping role can contribute to a more holistic appreciation of the complexity of technology introduction in healthcare settings. It can also prove practically useful in providing a theoretically informed approach to sampling (by drawing on informants that are related to the technology in question) and analysis (by providing a conceptual tool and vocabulary that can form the basis for interpretations). We draw on existing empirical work in this area and our ongoing work investigating the integration of electronic health record systems introduced as part of England's National Programme for Information Technology to illustrate salient points. Summary Actor-Network Theory needs to be used pragmatically with an appreciation of its shortcomings. Our experiences suggest it can be helpful in investigating technology implementations in healthcare settings. PMID:21040575
Cresswell, Kathrin M; Worth, Allison; Sheikh, Aziz
2010-11-01
Actor-Network Theory (ANT) is an increasingly influential, but still deeply contested, approach to understand humans and their interactions with inanimate objects. We argue that health services research, and in particular evaluations of complex IT systems in health service organisations, may benefit from being informed by Actor-Network Theory perspectives. Despite some limitations, an Actor-Network Theory-based approach is conceptually useful in helping to appreciate the complexity of reality (including the complexity of organisations) and the active role of technology in this context. This can prove helpful in understanding how social effects are generated as a result of associations between different actors in a network. Of central importance in this respect is that Actor-Network Theory provides a lens through which to view the role of technology in shaping social processes. Attention to this shaping role can contribute to a more holistic appreciation of the complexity of technology introduction in healthcare settings. It can also prove practically useful in providing a theoretically informed approach to sampling (by drawing on informants that are related to the technology in question) and analysis (by providing a conceptual tool and vocabulary that can form the basis for interpretations). We draw on existing empirical work in this area and our ongoing work investigating the integration of electronic health record systems introduced as part of England's National Programme for Information Technology to illustrate salient points. Actor-Network Theory needs to be used pragmatically with an appreciation of its shortcomings. Our experiences suggest it can be helpful in investigating technology implementations in healthcare settings.
Bassett, Danielle S; Mattar, Marcelo G
2017-04-01
Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.
ERIC Educational Resources Information Center
Latta, Rachel E.; Goodman, Lisa A.
2011-01-01
A large body of cross-sectional and longitudinal research demonstrates the important contribution of informal social networks to the well-being and safety of female survivors of intimate partner violence (IPV). Most survivors turn to family and friends before, during, and after their involvement with formal services; and many rely solely on…
ERIC Educational Resources Information Center
Latta, Rachel E.; Goodman, Lisa A.
2011-01-01
A large body of cross-sectional and longitudinal research demonstrates the important contribution of informal social networks to the well-being and safety of female survivors of intimate partner violence (IPV). Most survivors turn to family and friends before, during, and after their involvement with formal services; and many rely solely on…
NASA Astrophysics Data System (ADS)
Ferry, James P.; Lo, Darren; Ahearn, Stephen T.; Phillips, Aaron M.
Despite the breadth of modern network theory, it can be difficult to apply its results to the task of uncovering terrorist networks: the most useful network analyses are often low-tech, link-following approaches. In the traditional military domain, detection theory has a long history of finding stealthy targets such as submarines. We demonstrate how the detection theory framework leads to a variety of network analysis questions. Some solutions to these leverage existing theory; others require novel techniques - but in each case the solutions contribute to a principled methodology for solving network detection problems. This endeavor is difficult, and the work here represents only a beginning. However, the required mathematics is interesting, being the synthesis of two fields with little common history.
Unravelling the size distribution of social groups with information theory in complex networks
NASA Astrophysics Data System (ADS)
Hernando, A.; Villuendas, D.; Vesperinas, C.; Abad, M.; Plastino, A.
2010-07-01
The minimization of Fisher’s information (MFI) approach of Frieden et al. [Phys. Rev. E 60, 48 (1999)] is applied to the study of size distributions in social groups on the basis of a recently established analogy between scale invariant systems and classical gases [Phys. A 389, 490 (2010)]. Going beyond the ideal gas scenario is seen to be tantamount to simulating the interactions taking place, for a competitive cluster growth process, in a scale-free ideal network - a non-correlated network with a connection-degree’s distribution that mimics the scale-free ideal gas density distribution. We use a scaling rule that allows one to classify the final cluster-size distributions using only one parameter that we call the competitiveness, which can be seen as a measure of the strength of the interactions. We find that both empirical city-size distributions and electoral results can be thus reproduced and classified according to this competitiveness-parameter, that also allow us to infer the maximum number of stable social relationships that one person can maintain, known as the Dunbar number, together with its standard deviation. We discuss the importance of this number in connection with the empirical phenomenon known as “six-degrees of separation”. Finally, we show that scaled city-size distributions of large countries follow, in general, the same universal distribution.
NASA Astrophysics Data System (ADS)
Pham, H. V.; Tsai, F. T. C.
2014-12-01
Groundwater systems are complex and subject to multiple interpretations and conceptualizations due to a lack of sufficient information. As a result, multiple conceptual models are often developed and their mean predictions are preferably used to avoid biased predictions from using a single conceptual model. Yet considering too many conceptual models may lead to high prediction uncertainty and may lose the purpose of model development. In order to reduce the number of models, an optimal observation network design is proposed based on maximizing the Kullback-Leibler (KL) information to discriminate competing models. The KL discrimination function derived by Box and Hill [1967] for one additional observation datum at a time is expanded to account for multiple independent spatiotemporal observations. The Bayesian model averaging (BMA) method is used to incorporate existing data and quantify future observation uncertainty arising from conceptual and parametric uncertainties in the discrimination function. To consider the future observation uncertainty, the Monte Carlo realizations of BMA predicted future observations are used to calculate the mean and variance of posterior model probabilities of the competing models. The goal of the optimal observation network design is to find the number and location of observation wells and sampling rounds such that the highest posterior model probability of a model is larger than a desired probability criterion (e.g., 95%). The optimal observation network design is implemented to a groundwater study in the Baton Rouge area, Louisiana to collect new groundwater heads from USGS wells. The considered sources of uncertainty that create multiple groundwater models are the geological architecture, the boundary condition, and the fault permeability architecture. All possible design solutions are enumerated using high performance computing systems. Results show that total model variance (the sum of within-model variance and between
Information network architectures
NASA Technical Reports Server (NTRS)
Murray, N. D.
1985-01-01
Graphs, charts, diagrams and outlines of information relative to information network architectures for advanced aerospace missions, such as the Space Station, are presented. Local area information networks are considered a likely technology solution. The principle needs for the network are listed.
Tao, Ying; Li, Jianrong
2010-01-01
Motivation Despite advances in the gene annotation process, the functions of a large portion of the gene products remain insufficiently characterized. In addition, the “in silico” prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or function genomics approaches. Results We propose a novel approach, Information Theory-based Semantic Similarity (ITSS), to automatically predict molecular functions of genes based on Gene Ontology annotations. We have demonstrated using a 10-fold cross-validation that the ITSS algorithm obtains prediction accuracies (Precision 97%, Recall 77%) comparable to other machine learning algorithms when applied to similarly dense annotated portions of the GO datasets. In addition, such method can generate highly accurate predictions in sparsely annotated portions of GO, in which previous algorithm failed to do so. As a result, our technique generates an order of magnitude more gene function predictions than previous methods. Further, this paper presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions for an evaluation than generally used cross-validations type of evaluations. By manually assessing a random sample of 100 predictions conducted in a historical roll-back evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43%–58%) can be achieved for the human GO Annotation file dated 2003. Availability The program is available on request. The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset are available at http://phenos.bsd.uchicago.edu/mphenogo/prediction_result_2005.txt. PMID:17646340
Li, Haiquan; Lee, Younghee; Chen, James L; Rebman, Ellen; Li, Jianrong
2012-01-01
Objective Thousands of complex-disease single-nucleotide polymorphisms (SNPs) have been discovered in genome-wide association studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. The authors hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complex-disease traits and offer insights for drug repositioning. Methods Trait-to-polymorphism (SNPs) associations confirmed in GWAS were used. A novel method to quantify trait–trait similarity anchored in Gene Ontology annotations of human proteins and information theory was developed. The results were then validated with the shortest paths of physical protein interactions between biologically similar traits. Results A network was constructed consisting of 280 significant intertrait similarities among 177 disease traits, which covered 1438 well-validated disease-associated SNPs. Thirty-nine percent of intertrait connections were confirmed by curators, and the following additional studies demonstrated the validity of a proportion of the remainder. On a phenotypic trait level, higher Gene Ontology similarity between proteins correlated with smaller ‘shortest distance’ in protein interaction networks of complexly inherited diseases (Spearman p<2.2×10−16). Further, ‘cancer traits’ were similar to one another, as were ‘metabolic syndrome traits’ (Fisher's exact test p=0.001 and 3.5×10−7, respectively). Conclusion An imputed disease network by information-anchored functional similarity from GWAS trait-associated SNPs is reported. It is also demonstrated that small shortest paths of protein interactions correlate with complex-disease function. Taken together, these findings provide the framework for investigating drug targets with unbiased functional biomolecular networks rather than worn-out single-gene and subjective canonical pathway approaches
Goodall, K T; Newman, L A; Ward, P R
2014-11-01
Migrant well-being can be strongly influenced by the migration experience and subsequent degree of mainstream language acquisition. There is little research on how older Culturally And Linguistically Diverse (CALD) migrants who have 'aged in place' find health information, and the role which digital technology plays in this. Although the research for this paper was not focused on cancer, we draw out implications for providing cancer-related information to this group. We interviewed 54 participants (14 men and 40 women) aged 63-94 years, who were born in Italy or Greece, and who migrated to Australia mostly as young adults after World War II. Constructivist grounded theory and social network analysis were used for data analysis. Participants identified doctors, adult children, local television, spouse, local newspaper and radio as the most important information sources. They did not generally use computers, the Internet or mobile phones to access information. Literacy in their birth language, and the degree of proficiency in understanding and using English, influenced the range of information sources accessed and the means used. The ways in which older CALD migrants seek and access information has important implications for how professionals and policymakers deliver relevant information to them about cancer prevention, screening, support and treatment, particularly as information and resources are moved online as part of e-health. © 2014 John Wiley & Sons Ltd.
ERIC Educational Resources Information Center
Pettersson, Rune
2014-01-01
Information design has practical and theoretical components. As an academic discipline we may view information design as a combined discipline, a practical theory, or as a theoretical practice. So far information design has incorporated facts, influences, methods, practices, principles, processes, strategies, and tools from a large number of…
Constructor theory of information
Deutsch, David; Marletto, Chiara
2015-01-01
We propose a theory of information expressed solely in terms of which transformations of physical systems are possible and which are impossible—i.e. in constructor-theoretic terms. It includes conjectured, exact laws of physics expressing the regularities that allow information to be physically instantiated. Although these laws are directly about information, independently of the details of particular physical instantiations, information is not regarded as an a priori mathematical or logical concept, but as something whose nature and properties are determined by the laws of physics alone. This theory solves a problem at the foundations of existing information theory, namely that information and distinguishability are each defined in terms of the other. It also explains the relationship between classical and quantum information, and reveals the single, constructor-theoretic property underlying the most distinctive phenomena associated with the latter, including the lack of in-principle distinguishability of some states, the impossibility of cloning, the existence of pairs of variables that cannot simultaneously have sharp values, the fact that measurement processes can be both deterministic and unpredictable, the irreducible perturbation caused by measurement, and locally inaccessible information (as in entangled systems). PMID:25663803
Constructor theory of information.
Deutsch, David; Marletto, Chiara
2015-02-08
We propose a theory of information expressed solely in terms of which transformations of physical systems are possible and which are impossible-i.e. in constructor-theoretic terms. It includes conjectured, exact laws of physics expressing the regularities that allow information to be physically instantiated. Although these laws are directly about information, independently of the details of particular physical instantiations, information is not regarded as an a priori mathematical or logical concept, but as something whose nature and properties are determined by the laws of physics alone. This theory solves a problem at the foundations of existing information theory, namely that information and distinguishability are each defined in terms of the other. It also explains the relationship between classical and quantum information, and reveals the single, constructor-theoretic property underlying the most distinctive phenomena associated with the latter, including the lack of in-principle distinguishability of some states, the impossibility of cloning, the existence of pairs of variables that cannot simultaneously have sharp values, the fact that measurement processes can be both deterministic and unpredictable, the irreducible perturbation caused by measurement, and locally inaccessible information (as in entangled systems).
ERIC Educational Resources Information Center
National Public Telecomputing Network, Cleveland, OH.
This report describes the National Public Telecomputing Network's (NPTN) development of free, public-access, community computer systems throughout the United States. It also provides information on how to initiate a "Free-Net" through the Rural Information Network. Free-Nets are multi-user systems with some of the power and…
National Network for Immunization Information
... American College of Obstetricians and Gynecologists . © Copyright National Network for Immunization Information. The information contained in the National Network for Immunization Information Web site should not be ...
Information Networks in Biomedicine
ERIC Educational Resources Information Center
Millard, William L.
1975-01-01
Describes current biomedical information networks, focusing on those with an educational function, and elaborates on the problems encountered in planning, implementing, utilizing and evaluating such networks. Journal of Biocommunication, T. Banks, Educ. TV-431N, U. of Calif., San Francisco 94143. Subscription Rates: individuals and libraries,…
Congenital Heart Information Network
... Baemayr for The Congenital Heart Information Network Exempt organization under Section 501(c)3. Copyright ©1996 - 2016 C.H.I.N. All rights reserved TX4-390-685 Original site design and HTML by Panoptic Communications
NASA Astrophysics Data System (ADS)
Suhov, Y.
We begin with the definition of information gained by knowing that an event A has occurred: iota (A) = -log_2 {{P}}(A). (A dual point of view is also useful (although more evasive), where iota (A) is the amount of information needed to specify event A.) Here and below {{P}} stands for the underlying probability distribution. So the rarer an event A, the more information we gain if we know it has occurred. (More broadly, the rarer an event A, the more impact it will have. For example, the unlikely event that occurred in 1938 when fishermen caught a coelacanth - a prehistoric fish believed to be extinct - required a significant change to beliefs about evolution and biology. On the other hand, the likely event of catching a herring or a tuna would hardly imply any change in theories.)
An information theory account of cognitive control
Fan, Jin
2014-01-01
Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory. PMID:25228875
An information theory account of cognitive control.
Fan, Jin
2014-01-01
Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory.
Florida Information Resource Network.
ERIC Educational Resources Information Center
Watson, Francis C.
1986-01-01
The Florida Information Resource Network (FIRN) is an effort by the Florida education community and the Florida Legislature to provide an electronic link among all agencies, institutions, and schools in the public education system. The communications link, perhaps one of the most advanced in the nation, has three purposes: (1) to provide equal…
Information cascade on networks
NASA Astrophysics Data System (ADS)
Hisakado, Masato; Mori, Shintaro
2016-05-01
In this paper, we discuss a voting model by considering three different kinds of networks: a random graph, the Barabási-Albert (BA) model, and a fitness model. A voting model represents the way in which public perceptions are conveyed to voters. Our voting model is constructed by using two types of voters-herders and independents-and two candidates. Independents conduct voting based on their fundamental values; on the other hand, herders base their voting on the number of previous votes. Hence, herders vote for the majority candidates and obtain information relating to previous votes from their networks. We discuss the difference between the phases on which the networks depend. Two kinds of phase transitions, an information cascade transition and a super-normal transition, were identified. The first of these is a transition between a state in which most voters make the correct choices and a state in which most of them are wrong. The second is a transition of convergence speed. The information cascade transition prevails when herder effects are stronger than the super-normal transition. In the BA and fitness models, the critical point of the information cascade transition is the same as that of the random network model. However, the critical point of the super-normal transition disappears when these two models are used. In conclusion, the influence of networks is shown to only affect the convergence speed and not the information cascade transition. We are therefore able to conclude that the influence of hubs on voters' perceptions is limited.
Computer and information networks.
Greenberger, M; Aronofsky, J; McKenney, J L; Massy, W F
1973-10-05
The most basic conclusion coming out of the EDUCOM seminars is that computer networking must be acknowledged as an important new mode for obtaining information and computation (15). It is a real alternative that needs to be given serious attention in current planning and decision-making. Yet the fact is that many institutions are not taking account of networks when they confer on whether or how to replace their main computer. Articulation of the possibilities of computer networks goes back to the early 1960's and before, and working networks have been in evidence for several years now, both commercially and in universities. What is new, however, is the unmistakable recognition-bordering on a sense of the inevitable-that networks are finally practical and here to stay. The visionary and promotional phases of computer networks are over. It is time for hard-nosed comparative analysis (16). Another conclusion of the seminars has to do with the factors that hinder the fuller development of networking. The major problems to be overcome in applying networks to research and education are political, organizational, and economic in nature rather than technological. This is not to say that the hardware and software problems of linking computers and information systems are completely solved, but they are not the big bottlenecks at present. Research and educational institutions must find ways to organize themselves as well as their computers to work together for greater resource sharing. The coming of age of networks takes on special significance as a result of widespread dissatisfactions expressed with the present computing situation. There is a feeling that the current mode of autonomous, self-sufficient operation in the provision of computing and information services is frequently wasteful, deficient, and unresponsive to users' needs because of duplication of effort from one installation to another, incompatibilities, and inadequate documentation, program support, and user
Dynamic information routing in complex networks
NASA Astrophysics Data System (ADS)
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2015-03-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how information may be specifically communicated and dynamically routed in these systems is not well understood. Here we demonstrate that collective dynamical states systematically control patterns of information sharing and transfer in networks, as measured by delayed mutual information and transfer entropies between activities of a network's units. For oscillatory networks we analyze how individual unit properties, the connectivity structure and external inputs all provide means to flexibly control information routing. For multi-scale, modular architectures, we resolve communication patterns at all levels and show how local interventions within one sub-network may remotely control the non-local network-wide routing of information. This theory helps understanding information routing patterns across systems where collective dynamics co-occurs with a communication function.
Network theory for inhomogeneous thermoelectrics
NASA Astrophysics Data System (ADS)
Angst, Sebastian; Wolf, Dietrich E.
2016-04-01
The Onsager-de Groot-Callen transport theory, implemented as a network model, is used to simulate the transient Harman method, which is widely used experimentally to determine all thermoelectric transport coefficients in a single measurement setup. It is shown that this method systematically overestimates the Seebeck coefficient for samples composed of two different materials. As a consequence, the figure of merit is also overestimated, if the thermal coupling of the measurement setup to the environment is weak. For a mixture of metal and semiconductor particles near metal percolation the figure of merit obtained by the Harman method is more than 100% too large. For a correct interpretation of the experimental data, information on composition and microstructure of the sample are indispensable.
NASA Astrophysics Data System (ADS)
Kim, J.; Woo, N. C.; Kim, S.; Yun, J.; Kim, S.; Kang, M.; Cho, C. H.; Chun, J. H.
2014-12-01
We demonstrate how field measurements can inform the selection of model frameworks in small watershed applications. Based on the assumption that ecohydrological systems are open and complex, we employ the process network analysis to identify the system state and the subsystems architecture with changing environment conditions. Ecohydrological and biogeochemical processes in a watershed can be viewed as a network of processes of a wide range of scales involving various feedback loops and time delay. Using the KoFlux tower-based measurements of energy, water and CO2 flux time series along with those representing the soil-plant-atmospheric continuum; we evaluated statistical measures of characterizing the organization of the information flows in the system. We used Shannon's information entropy and calculated the mutual information and transfer entropy, following Ruddell and Kumar (2009). Transfer entropy can measure the relative strength and time scale of couplings between the variables. In this analysis, we selected 15 variables associated with ecohydrological processes, which are groundwater table height, water temperature, specific conductivity, soil moisture contents at three depths, ecosystem respiration, gross primary productivity, sensible heat flux, latent heat flux, precipitation, air temperature, vapor pressure deficit, atmospheric pressure, and solar radiation. The data-driven nature of this investigation may shed a light on reconciling model parsimony with equifinality in small watershed applications. (Acknowledgment: This work and the data used in the study were funded by the Korea Meteorological Administration Research and Development Program under Grant Weather Information Service Engine (WISE) project,153-3100-3133-302-350 and Grant CATER 2014-3030, respectively. The KoFlux site was supported by the Long-term Ecological Study and Monitoring of Forest Ecosystem Project of Korea Forest Research Institute.)
Information theoretic description of networks
NASA Astrophysics Data System (ADS)
Wilhelm, Thomas; Hollunder, Jens
2007-11-01
We present a new information theoretic approach for network characterizations. It is developed to describe the general type of networks with n nodes and L directed and weighted links, i.e., it also works for the simpler undirected and unweighted networks. The new information theoretic measures for network characterizations are based on a transmitter-receiver analogy of effluxes and influxes. Based on these measures, we classify networks as either complex or non-complex and as either democracy or dictatorship networks. Directed networks, in particular, are furthermore classified as either information spreading and information collecting networks. The complexity classification is based on the information theoretic network complexity measure medium articulation (MA). It is proven that special networks with a medium number of links ( L∼n1.5) show the theoretical maximum complexity MA=(log n)2/2. A network is complex if its MA is larger than the average MA of appropriately randomized networks: MA>MAr. A network is of the democracy type if its redundancy R
Quantum Theory is an Information Theory
NASA Astrophysics Data System (ADS)
D'Ariano, Giacomo M.; Perinotti, Paolo
2016-03-01
In this paper we review the general framework of operational probabilistic theories (OPT), along with the six axioms from which quantum theory can be derived. We argue that the OPT framework along with a relaxed version of five of the axioms, define a general information theory. We close the paper with considerations about the role of the observer in an OPT, and the interpretation of the von Neumann postulate and the Schrödinger-cat paradox.
Graphical Model Theory for Wireless Sensor Networks
Davis, William B.
2002-12-08
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
Network Security Validation Using Game Theory
NASA Astrophysics Data System (ADS)
Papadopoulou, Vicky; Gregoriades, Andreas
Non-functional requirements (NFR) such as network security recently gained widespread attention in distributed information systems. Despite their importance however, there is no systematic approach to validate these requirements given the complexity and uncertainty characterizing modern networks. Traditionally, network security requirements specification has been the results of a reactive process. This however, limited the immunity property of the distributed systems that depended on these networks. Security requirements specification need a proactive approach. Networks' infrastructure is constantly under attack by hackers and malicious software that aim to break into computers. To combat these threats, network designers need sophisticated security validation techniques that will guarantee the minimum level of security for their future networks. This paper presents a game-theoretic approach to security requirements validation. An introduction to game theory is presented along with an example that demonstrates the application of the approach.
Building a Shared Information Network.
ERIC Educational Resources Information Center
Stanat, Ruth
1991-01-01
Discussion of information needs in a business environment focuses on how to build a shared information network. Highlights include the evolution of corporate intelligence systems; results of a survey that examined the information networking needs of large corporations; and a case study of the development of an information network at Citibank N.A.…
Psychology and social networks: a dynamic network theory perspective.
Westaby, James D; Pfaff, Danielle L; Redding, Nicholas
2014-04-01
Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
On directed information theory and Granger causality graphs.
Amblard, Pierre-Olivier; Michel, Olivier J J
2011-02-01
Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.
NASA Astrophysics Data System (ADS)
Ginsparg, Paul
I review the background and some recent trends of a particular scholarly information network, arXiv.org, and discuss some of its implications for new scholarly publication models. If we were to start from scratch today to design a quality-controlled archive and distribution system for scientific and technical information, it could take a very different form from what has evolved in the past decade from pre-existing print infrastructure. Near-term advances in automated classification systems, authoring tools, and document formats will facilitate efficient datamining and long-term archival stability, and I discuss how these could provide not only more efficient means of accessing and navigating the information, but also more cost-effective means of authentication and quality control. Finally, I illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its network of practitioners.
Information Recovery in Behavioral Networks
Squartini, Tiziano; Ser-Giacomi, Enrico; Garlaschelli, Diego; Judge, George
2015-01-01
In the context of agent based modeling and network theory, we focus on the problem of recovering behavior-related choice information from origin-destination type data, a topic also known under the name of network tomography. As a basis for predicting agents' choices we emphasize the connection between adaptive intelligent behavior, causal entropy maximization, and self-organized behavior in an open dynamic system. We cast this problem in the form of binary and weighted networks and suggest information theoretic entropy-driven methods to recover estimates of the unknown behavioral flow parameters. Our objective is to recover the unknown behavioral values across the ensemble analytically, without explicitly sampling the configuration space. In order to do so, we consider the Cressie-Read family of entropic functionals, enlarging the set of estimators commonly employed to make optimal use of the available information. More specifically, we explicitly work out two cases of particular interest: Shannon functional and the likelihood functional. We then employ them for the analysis of both univariate and bivariate data sets, comparing their accuracy in reproducing the observed trends. PMID:25946169
Geoscience Information Network
NASA Astrophysics Data System (ADS)
Allison, M. L.; Gundersen, L. C.
2007-12-01
Geological surveys in the USA have an estimated 2,000-3,000 databases that represent one of the largest, long- term information resources on the geology of the United States and collectively constitute a national geoscience data "backbone" for research and applications. An NSF-supported workshop in February, 2007, among representatives of the Association of American State Geologists (AASG) and the USGS, recommended that "the nation's geological surveys develop a national geoscience information framework that is distributed, interoperable, uses open source standards and common protocols, respects and acknowledges data ownership, fosters communities of practice to grow, and develops new web services and clients." The AASG and USGS have formally endorsed the workshop recommendations and formed a joint Steering Committee to pursue design and implementation of the Geoscience Information Network (GIN). GIN is taking a modular approach in assembling the network: 1. Agreement on open-source standards and common protocols through the use of Open Geospatial Consortium (OGC) standards. 2. A data exchange model utilizing the geoscience mark-up language GeoSciML, an OGC GML-based application. 3. A prototype data discovery tool (National Digital Catalogue - NDC) developing under the National Geological and Geophysical Data Preservation Program run by the USGS. 4. Data integration tools developed or planned by a number of independent projects. A broader NSF-sponsored workshop in March 2007 examined what direction the geoinformatics community in the US should take towards developing a National Geoinformatics System. The final report stated that, "It was clear that developing such a system should involve a partnership between academia, government, and industry that should be closely connected to the efforts of the U. S. Geological Survey and the state geological surveys..." The GIN is collaborating with 1-G Europe, a coalition of 27 European geological surveys in the One
Network Centric Information Structure - Crisis Information Management
2004-06-01
information management during emergency and crisis. The paper presents a prioritized set of important information elements that would be of value during a crisis or a rescue mission. It suggests how the information should be collected, stored and distributed, and it suggests information distribution methods supporting a network centric information structure concept. The work is funded by Teleplan and the Norwegian Research
Potential theory for directed networks.
Zhang, Qian-Ming; Lü, Linyuan; Wang, Wen-Qiang; Zhu, Yu-Xiao; Zhou, Tao
2013-01-01
Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation.
Potential Theory for Directed Networks
Zhang, Qian-Ming; Lü, Linyuan; Wang, Wen-Qiang; Zhou, Tao
2013-01-01
Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation. PMID:23408979
THE INFORMATION THEORY ASPECT OF TELEPATHY,
INFORMATION THEORY, COMMUNICATION AND RADIO SYSTEMS, PARAPSYCHOLOGY , INFORMATION THEORY, SOCIAL COMMUNICATION, PROBABILITY, ELECTROMAGNETIC RADIATION, PHYSIOLOGY, ALGORITHMS, EXPERIMENTAL DATA, USSR.
ERIC Educational Resources Information Center
Thornberg, Robert
2012-01-01
There is a widespread idea that in grounded theory (GT) research, the researcher has to delay the literature review until the end of the analysis to avoid contamination--a dictum that might turn educational researchers away from GT. Nevertheless, in this article the author (a) problematizes the dictum of delaying a literature review in classic…
ERIC Educational Resources Information Center
Thornberg, Robert
2012-01-01
There is a widespread idea that in grounded theory (GT) research, the researcher has to delay the literature review until the end of the analysis to avoid contamination--a dictum that might turn educational researchers away from GT. Nevertheless, in this article the author (a) problematizes the dictum of delaying a literature review in classic…
Extracting information from multiplex networks
NASA Astrophysics Data System (ADS)
Iacovacci, Jacopo; Bianconi, Ginestra
2016-06-01
Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ ˜ S for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.
Extracting information from multiplex networks.
Iacovacci, Jacopo; Bianconi, Ginestra
2016-06-01
Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.
INFORMATION: THEORY, BRAIN, AND BEHAVIOR
Jensen, Greg; Ward, Ryan D.; Balsam, Peter D.
2016-01-01
In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified. PMID:24122456
Information: theory, brain, and behavior.
Jensen, Greg; Ward, Ryan D; Balsam, Peter D
2013-11-01
In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified. © Society for the Experimental Analysis of Behavior.
Information Networking in Population Education.
ERIC Educational Resources Information Center
United Nations Educational, Scientific, and Cultural Organization, Bangkok (Thailand). Regional Office for Education in Asia and the Pacific.
The rapidly increasing body of knowledge in population education has created the need for systematic and effective information services. Information networking entails sharing resources so that the information needs of all network participants are met. The goals of this manual are to: (1) instill in population education specialists a more…
NASA Technical Reports Server (NTRS)
1975-01-01
Formalized technical reporting is described and indexed, which resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. The five classes of publications included are technical reports, technical memorandums, articles from the bimonthly Deep Space Network Progress Report, special publications, and articles published in the open literature. The publications are indexed by author, subject, and publication type and number.
Interdisciplinary and physics challenges of network theory
NASA Astrophysics Data System (ADS)
Bianconi, Ginestra
2015-09-01
Network theory has unveiled the underlying structure of complex systems such as the Internet or the biological networks in the cell. It has identified universal properties of complex networks, and the interplay between their structure and dynamics. After almost twenty years of the field, new challenges lie ahead. These challenges concern the multilayer structure of most of the networks, the formulation of a network geometry and topology, and the development of a quantum theory of networks. Making progress on these aspects of network theory can open new venues to address interdisciplinary and physics challenges including progress on brain dynamics, new insights into quantum technologies, and quantum gravity.
The tensor network theory library
NASA Astrophysics Data System (ADS)
Al-Assam, S.; Clark, S. R.; Jaksch, D.
2017-09-01
In this technical paper we introduce the tensor network theory (TNT) library—an open-source software project aimed at providing a platform for rapidly developing robust, easy to use and highly optimised code for TNT calculations. The objectives of this paper are (i) to give an overview of the structure of TNT library, and (ii) to help scientists decide whether to use the TNT library in their research. We show how to employ the TNT routines by giving examples of ground-state and dynamical calculations of one-dimensional bosonic lattice system. We also discuss different options for gaining access to the software available at www.tensornetworktheory.org.
Information thermodynamics on causal networks.
Ito, Sosuke; Sagawa, Takahiro
2013-11-01
We study nonequilibrium thermodynamics of complex information flows induced by interactions between multiple fluctuating systems. Characterizing nonequilibrium dynamics by causal networks (i.e., Bayesian networks), we obtain novel generalizations of the second law of thermodynamics and the fluctuation theorem, which include an informational quantity characterized by the topology of the causal network. Our result implies that the entropy production in a single system in the presence of multiple other systems is bounded by the information flow between these systems. We demonstrate our general result by a simple model of biochemical adaptation.
Epistasis analysis using information theory.
Moore, Jason H; Hu, Ting
2015-01-01
Here we introduce entropy-based measures derived from information theory for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the methods and highlight some of the modifications that have greatly improved its power for genetic analysis. We end with a few published studies of complex human diseases that have used these measures.
World-Wide Information Networks.
ERIC Educational Resources Information Center
Samuelson, Kjell A. H. W.
The future paths of research and development towards world-wide, automated information networks in full operation are examined. From international networked planning and projects under way it appears that exploratory as well as normative approaches have been taken. To some extent adequate technolgical facilities have already come into existence…
Building a Unified Information Network.
ERIC Educational Resources Information Center
Avram, Henriette D.
1988-01-01
Discusses cooperative efforts between research organizations and libraries to create a national information network. Topics discussed include the Linked System Project (LSP); technical processing versus reference and research functions; Open Systems Interconnection (OSI) Reference Model; the National Science Foundation Network (NSFNET); and…
Quantum Information Theory - an Invitation
NASA Astrophysics Data System (ADS)
Werner, Reinhard F.
Quantum information and quantum computers have received a lot of public attention recently. Quantum computers have been advertised as a kind of warp drive for computing, and indeed the promise of the algorithms of Shor and Grover is to perform computations which are extremely hard or even provably impossible on any merely ``classical'' computer.In this article I shall give an account of the basic concepts of quantum information theory is given, staying as much as possible in the area of general agreement.The article is divided into two parts. The first (up to the end of Sect. 2.5) is mostly in plain English, centered around the exploration of what can or cannot be done with quantum systems as information carriers. The second part, Sect. 2.6, then gives a description of the mathematical structures and of some of the tools needed to develop the theory.
Unification of quantum information theory
NASA Astrophysics Data System (ADS)
Abeyesinghe, Anura
We present the unification of many previously disparate results in noisy quantum Shannon theory and the unification of all of noiseless quantum Shannon theory. More specifically we deal here with bipartite, unidirectional, and memoryless quantum Shannon theory. We find all the optimal protocols and quantify the relationship between the resources used, both for the one-shot and for the ensemble case, for what is arguably the most fundamental task in quantum information theory: sharing entangled states between a sender and a receiver. We find that all of these protocols are derived from our one-shot superdense coding protocol and relate nicely to each other. We then move on to noisy quantum information theory and give a simple, direct proof of the "mother" protocol, or rather her generalization to the Fully Quantum Slepian-Wolf protocol (FQSW). FQSW simultaneously accomplishes two goals: quantum communication-assisted entanglement distillation, and state transfer from the sender to the receiver. As a result, in addition to her other "children," the mother protocol generates the state merging primitive of Horodecki, Oppenheim, and Winter as well as a new class of distributed compression protocols for correlated quantum sources, which are optimal for sources described by separable density operators. Moreover, the mother protocol described here is easily transformed into the so-called "father" protocol, demonstrating that the division of single-sender/single-receiver protocols into two families was unnecessary: all protocols in the family are children of the mother.
Network Information Management Subsystem
NASA Technical Reports Server (NTRS)
Chatburn, C. C.
1985-01-01
The Deep Space Network is implementing a distributed data base management system in which the data are shared among several applications and the host machines are not totally dedicated to a particular application. Since the data and resources are to be shared, the equipment must be operated carefully so that the resources are shared equitably. The current status of the project is discussed and policies, roles, and guidelines are recommended for the organizations involved in the project.
Dempster-Shafer theory and connections to information theory
NASA Astrophysics Data System (ADS)
Peri, Joseph S. J.
2013-05-01
The Dempster-Shafer theory is founded on probability theory. The entire machinery of probability theory, and that of measure theory, is at one's disposal for the understanding and the extension of the Dempster-Shafer theory. It is well known that information theory is also founded on probability theory. Claude Shannon developed, in the 1940's, the basic concepts of the theory and demonstrated their utility in communications and coding. Shannonian information theory is not, however, the only type of information theory. In the 1960's and 1970's, further developments in this field were made by French and Italian mathematicians. They developed information theory axiomatically, and discovered not only the Wiener- Shannon composition law, but also the hyperbolic law and the Inf-law. The objective of this paper is to demonstrate the mathematical connections between the Dempster Shafer theory and the various types of information theory. A simple engineering example will be used to demonstrate the utility of the concepts.
Studies in quantum information theory
NASA Astrophysics Data System (ADS)
Menicucci, Nicolas C.
Quantum information theory started as the backdrop for quantum computing and is often considered only in relation to this technology, which is still in its infancy. But quantum information theory is only partly about quantum computing. While much of the interest in this field is spurred by the possible use of quantum computers for code breaking using fast factoring algorithms, to a physicist interested in deeper issues, it presents an entirely new set of questions based on an entirely different way of looking at the quantum world. This thesis is an exploration of several topics in quantum information theory. But it is also more than this. This thesis explores the new paradigm brought about by quantum information theory---that of physics as the flow of information. The thesis consists of three main parts. The first part describes my work on continuous-variable cluster states, a new platform for quantum computation. This begins with background material discussing classical and quantum computation and emphasizing the physical underpinnings of each, followed by a discussion of two recent unorthodox models of quantum computation. These models are combined into an original proposal for quantum computation using continuous-variable cluster states, including a proposed optical implementation. These are followed by a mathematical result radically simplifying the optical construction. Subsequent work simplifies this connection even further and provides a constructive proposal for scalable generation of large-scale cluster states---necessary if there is to be any hope of using this method in practical quantum computation. Experimental implementation is currently underway by my collaborators at The University of Virginia. The second part describes my work related to the physics of trapped ions, starting with an overview of the basic theory of linear ion traps. Although ion traps are often discussed in terms of their potential use for quantum computation, my work looks at their
Advances in the Theory of Complex Networks
NASA Astrophysics Data System (ADS)
Peruani, Fernando
An exhaustive and comprehensive review on the theory of complex networks would imply nowadays a titanic task, and it would result in a lengthy work containing plenty of technical details of arguable relevance. Instead, this chapter addresses very briefly the ABC of complex network theory, visiting only the hallmarks of the theoretical founding, to finally focus on two of the most interesting and promising current research problems: the study of dynamical processes on transportation networks and the identification of communities in complex networks.
ERIC Educational Resources Information Center
Zhang, Zheng; Heydon, Rachel
2016-01-01
This paper concerns an exploratory and interpretive case study of the literacy curricula in a Canadian transnational education programme (Pseudonym: SCS) delivered in China where Ontario secondary school curricula were used at the same time as the Chinese national high school curricula. Using ethnographic tools and actor-network theory, the study…
ERIC Educational Resources Information Center
Zhang, Zheng; Heydon, Rachel
2016-01-01
This paper concerns an exploratory and interpretive case study of the literacy curricula in a Canadian transnational education programme (Pseudonym: SCS) delivered in China where Ontario secondary school curricula were used at the same time as the Chinese national high school curricula. Using ethnographic tools and actor-network theory, the study…
Ranking Information in Networks
NASA Astrophysics Data System (ADS)
Eliassi-Rad, Tina; Henderson, Keith
Given a network, we are interested in ranking sets of nodes that score highest on user-specified criteria. For instance in graphs from bibliographic data (e.g. PubMed), we would like to discover sets of authors with expertise in a wide range of disciplines. We present this ranking task as a Top-K problem; utilize fixed-memory heuristic search; and present performance of both the serial and distributed search algorithms on synthetic and real-world data sets.
Workplace Learning in Informal Networks
ERIC Educational Resources Information Center
Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush
2014-01-01
Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that…
Information communication on complex networks
NASA Astrophysics Data System (ADS)
Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki
2013-02-01
Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.
Medina, K.D.; Tasker, Gary D.
1987-01-01
This report documents the results of an analysis of the surface-water data network in Kansas for its effectiveness in providing regional streamflow information. The network was analyzed using generalized least squares regression. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-, low-, and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow-gaging-station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations, and (or) adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and for discontinued stations for which unregulated flow characteristics, as well as physical and climatic characteristics, were available. The State was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for the three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean-square error for each cost level could be obtained by adding new stations and discontinuing some current network stations. Large reductions in sampling mean-square error for low-flow information could be achieved in all three network areas, the reduction in western Kansas being the most dramatic. The addition of new stations would be most beneficial for mean-flow information in western Kansas. The reduction of sampling mean-square error for high-flow information would benefit most from the addition of new stations in western Kansas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas
Network of Interdependent Networks: Overview of Theory and Applications
NASA Astrophysics Data System (ADS)
Kenett, Dror Y.; Gao, Jianxi; Huang, Xuqing; Shao, Shuai; Vodenska, Irena; Buldyrev, Sergey V.; Paul, Gerald; Stanley, H. Eugene; Havlin, Shlomo
Complex networks appear in almost every aspect of science and technology. Previous work in network theory has focused primarily on analyzing single networks that do not interact with other networks, despite the fact that many real-world networks interact with and depend on each other. Very recently an analytical framework for studying the percolation properties of interacting networks has been introduced. Here we review the analytical framework and the results for percolation laws for a network of networks (NON) formed by n interdependent random networks. The percolation properties of a network of networks differ greatly from those of single isolated networks. In particular, although networks with broad degree distributions, e.g., scale-free networks, are robust when analyzed as single networks, they become vulnerable in a NON. Moreover, because the constituent networks of a NON are connected by node dependencies, a NON is subject to cascading failure. When there is strong interdependent coupling between networks, the percolation transition is discontinuous (is a first-order transition), unlike the well-known continuous second-order transition in single isolated networks. We also review some possible real-world applications of NON theory.
Complex Networks: from Graph Theory to Biology
NASA Astrophysics Data System (ADS)
Lesne, Annick
2006-12-01
The aim of this text is to show the central role played by networks in complex system science. A remarkable feature of network studies is to lie at the crossroads of different disciplines, from mathematics (graph theory, combinatorics, probability theory) to physics (statistical physics of networks) to computer science (network generating algorithms, combinatorial optimization) to biological issues (regulatory networks). New paradigms recently appeared, like that of ‘scale-free networks’ providing an alternative to the random graph model introduced long ago by Erdös and Renyi. With the notion of statistical ensemble and methods originally introduced for percolation networks, statistical physics is of high relevance to get a deep account of topological and statistical properties of a network. Then their consequences on the dynamics taking place in the network should be investigated. Impact of network theory is huge in all natural sciences, especially in biology with gene networks, metabolic networks, neural networks or food webs. I illustrate this brief overview with a recent work on the influence of network topology on the dynamics of coupled excitable units, and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.
Application of Information Integration Theory to Methodology of Theory Development.
ERIC Educational Resources Information Center
Shanteau, James
Information integration theory (IIT) seeks to develop a unified theory of judgment and behavior. This theory provides a conceptual framework that has been applied to a variety of research areas including personality impression formation and decision making. In these applications information integration theory has helped to resolve methodological…
Recoverability in quantum information theory
NASA Astrophysics Data System (ADS)
Wilde, Mark
The fact that the quantum relative entropy is non-increasing with respect to quantum physical evolutions lies at the core of many optimality theorems in quantum information theory and has applications in other areas of physics. In this work, we establish improvements of this entropy inequality in the form of physically meaningful remainder terms. One of the main results can be summarized informally as follows: if the decrease in quantum relative entropy between two quantum states after a quantum physical evolution is relatively small, then it is possible to perform a recovery operation, such that one can perfectly recover one state while approximately recovering the other. This can be interpreted as quantifying how well one can reverse a quantum physical evolution. Our proof method is elementary, relying on the method of complex interpolation, basic linear algebra, and the recently introduced Renyi generalization of a relative entropy difference. The theorem has a number of applications in quantum information theory, which have to do with providing physically meaningful improvements to many known entropy inequalities. This is based on arXiv:1505.04661, now accepted for publication in Proceedings of the Royal Society A. I acknowledge support from startup funds from the Department of Physics and Astronomy at LSU, the NSF under Award No. CCF-1350397, and the DARPA Quiness Program through US Army Research Office award W31P4Q-12-1-0019.
Social Network Theory and Educational Change
ERIC Educational Resources Information Center
Daly, Alan J., Ed.
2010-01-01
"Social Network Theory and Educational Change" offers a provocative and fascinating exploration of how social networks in schools can impede or facilitate the work of education reform. Drawing on the work of leading scholars, the book comprises a series of studies examining networks among teachers and school leaders, contrasting formal…
Social Network Theory and Educational Change
ERIC Educational Resources Information Center
Daly, Alan J., Ed.
2010-01-01
"Social Network Theory and Educational Change" offers a provocative and fascinating exploration of how social networks in schools can impede or facilitate the work of education reform. Drawing on the work of leading scholars, the book comprises a series of studies examining networks among teachers and school leaders, contrasting formal…
Beyond mean field theory: statistical field theory for neural networks
Buice, Michael A; Chow, Carson C
2014-01-01
Mean field theories have been a stalwart for studying the dynamics of networks of coupled neurons. They are convenient because they are relatively simple and possible to analyze. However, classical mean field theory neglects the effects of fluctuations and correlations due to single neuron effects. Here, we consider various possible approaches for going beyond mean field theory and incorporating correlation effects. Statistical field theory methods, in particular the Doi–Peliti–Janssen formalism, are particularly useful in this regard. PMID:25243014
Information Theory, Inference and Learning Algorithms
NASA Astrophysics Data System (ADS)
Mackay, David J. C.
2003-10-01
Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.
Whether information network supplements friendship network
NASA Astrophysics Data System (ADS)
Miao, Lili; Zhang, Qian-Ming; Nie, Da-Cheng; Cai, Shi-Min
2015-02-01
Homophily is a significant mechanism for link prediction in complex network, of which principle describes that people with similar profiles or experiences tend to tie with each other. In a multi-relationship network, friendship among people has been utilized to reinforce similarity of taste for recommendation system whose basic idea is similar to homophily, yet how the taste inversely affects friendship prediction is little discussed. This paper contributes to address the issue by analyzing two benchmark data sets both including user's behavioral information of taste and friendship based on the principle of homophily. It can be found that the creation of friendship tightly associates with personal taste. Especially, the behavioral information of taste involving with popular objects is much more effective to improve the performance of friendship prediction. However, this result seems to be contradictory to the finding in Zhang et al. (2013) that the behavior information of taste involving with popular objects is redundant in recommendation system. We thus discuss this inconformity to comprehensively understand the correlation between them.
College Students' Nutrition Information Networks.
ERIC Educational Resources Information Center
Hertzler, Ann A.; Frary, Robert B.
1995-01-01
Use of nutrition information networks (consumer market, media, authority, family, and high school classes), food choices, fat practices, and nutrient intake were rated by 179 male and 300 female undergraduates. Family was an important influence; media and consumer market influenced fat practices, especially for women. No source was used very…
Network susceptibilities: Theory and applications.
Manik, Debsankha; Rohden, Martin; Ronellenfitsch, Henrik; Zhang, Xiaozhu; Hallerberg, Sarah; Witthaut, Dirk; Timme, Marc
2017-01-01
We introduce the concept of network susceptibilities quantifying the response of the collective dynamics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase-oscillator models, power grid models, and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibilities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.
Mathematical Theory of Neural Networks
1994-08-31
This report provides a summary of the grant work by the principal investigators in the area of neural networks . The topics covered deal with...properties) for nets; and the use of neural networks for the control of nonlinear systems.
Network susceptibilities: Theory and applications
NASA Astrophysics Data System (ADS)
Manik, Debsankha; Rohden, Martin; Ronellenfitsch, Henrik; Zhang, Xiaozhu; Hallerberg, Sarah; Witthaut, Dirk; Timme, Marc
2017-01-01
We introduce the concept of network susceptibilities quantifying the response of the collective dynamics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase-oscillator models, power grid models, and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibilities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.
Brain and cognitive reserve: Translation via network control theory.
Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H; Thompson-Schill, Sharon L; Bassett, Danielle S
2017-04-01
Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.
Information transmission in genetic regulatory networks: a review
NASA Astrophysics Data System (ADS)
Tkačik, Gašper; Walczak, Aleksandra M.
2011-04-01
Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.
Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias
2013-04-24
Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.
The application of information theory to biochemical signaling systems.
Rhee, Alex; Cheong, Raymond; Levchenko, Andre
2012-08-01
Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.
Networking Theories by Iterative Unpacking
ERIC Educational Resources Information Center
Koichu, Boris
2014-01-01
An iterative unpacking strategy consists of sequencing empirically-based theoretical developments so that at each step of theorizing one theory serves as an overarching conceptual framework, in which another theory, either existing or emerging, is embedded in order to elaborate on the chosen element(s) of the overarching theory. The strategy is…
Coding Theory Information Theory and Radar
2005-01-01
decision making, contingency perception, impulse control, cooperation and other prosocial behavior , etc. * The second component will consist of the...not guarantee optimal behavior for the group and vice versa. Even if an "optimal" solution is feasible, it may be based on assumptions that are...coherent and coordinated behavior even in the face of adverse circumstances. Satisficing game theory (SGT), which is summarized by Stirling, Frost, and
A network theory of mental disorders
Borsboom, Denny
2017-01-01
In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self‐sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines. PMID:28127906
A Complexity Theory of Neural Networks
1991-08-09
Significant progress has been made in laying the foundations of a complexity theory of neural networks . The fundamental complexity classes have been...identified and studied. The class of problems solvable by small, shallow neural networks has been found to be the same class even if (1) probabilistic...behaviour (2)Multi-valued logic, and (3)analog behaviour, are allowed (subject to certain resonable technical assumptions). Neural networks can be
Information Security and Privacy in Network Environments.
ERIC Educational Resources Information Center
Congress of the U.S., Washington, DC. Office of Technology Assessment.
The use of information networks for business and government is expanding enormously. Government use of networks features prominently in plans to make government more efficient, effective, and responsive. But the transformation brought about by the networking also raises new concerns for the security and privacy of networked information. This…
Information transport in multiplex networks
NASA Astrophysics Data System (ADS)
Pu, Cunlai; Li, Siyuan; Yang, Xianxia; Yang, Jian; Wang, Kai
2016-04-01
In this paper, we study information transport in multiplex networks comprised of two coupled subnetworks. The upper subnetwork, called the logical layer, employs the shortest paths protocol to determine the logical paths for packets transmission, while the lower subnetwork acts as the physical layer, in which packets are delivered by the biased random walk mechanism characterized with a parameter α. Through simulation, we obtain the optimal α corresponding to the maximum network lifetime and the maximum number of the arrival packets. Assortative coupling is better than random coupling and disassortative coupling, since it achieves better transmission performance. Generally, the more homogeneous the lower subnetwork is, the better the transmission performance, which is the opposite for the upper subnetwork. Finally, we propose an attack centrality for nodes based on the topological information of both subnetworks, and investigate the transmission performance under targeted attacks. Our work aids in understanding the spread and robustness issues of multiplex networks and provides some clues about the design of more efficient and robust routing architectures in communication systems.
Information Theory in Biology after 18 Years
ERIC Educational Resources Information Center
Johnson, Horton A.
1970-01-01
Reviews applications of information theory to biology, concluding that they have not proved very useful. Suggests modifications and extensions to increase the biological relevance of the theory, and speculates about applications in quantifying cell proliferation, chemical homeostasis and aging. (EB)
Information Theory in Biology after 18 Years
ERIC Educational Resources Information Center
Johnson, Horton A.
1970-01-01
Reviews applications of information theory to biology, concluding that they have not proved very useful. Suggests modifications and extensions to increase the biological relevance of the theory, and speculates about applications in quantifying cell proliferation, chemical homeostasis and aging. (EB)
Queuing theory models for computer networks
NASA Technical Reports Server (NTRS)
Galant, David C.
1989-01-01
A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.
Assessing Connections Between Behavior Change Theories Using Network Analysis.
Gainforth, Heather L; West, Robert; Michie, Susan
2015-10-01
A cross-disciplinary scoping review identified 83 of behavior change theories, with many similarities and overlapping constructs. Investigating the derivation of these theories may provide further understanding of their contribution and intended application. To develop and apply a method to describe the explicit derivation of theories of behavior change. A network analysis of the explicit "contributing to" relations between the 83 theories was conducted. Identification of relations involved textual analysis of primary theory sources. One hundred and twenty-two connections between the theories were identified amounting to 1.8% of the number possible. On average, theories contributed to one or two theories (mean = 1.47 ± 3.69 contributions) and were informed by one or two theories (mean = 1.47 ± 1.61 contributing theories). Most behavior change theories appear to be explicitly informed by few prior theories. If confirmed, this suggests a considerable dislocation between generations of theories which would be expected to undermine scientific progress.
Transmission of information in active networks
NASA Astrophysics Data System (ADS)
Baptista, M. S.; Kurths, J.
2008-02-01
Shannon’s capacity theorem is the main concept behind the theory of communication. It says that if the amount of information contained in a signal is smaller than the channel capacity of a physical media of communication, it can be transmitted with arbitrarily small probability of error. This theorem is usually applicable to ideal channels of communication in which the information to be transmitted does not alter the passive characteristics of the channel that basically tries to reproduce the source of information. For an active channel, a network formed by elements that are dynamical systems (such as neurons, chaotic or periodic oscillators), it is unclear if such theorem is applicable, once an active channel can adapt to the input of a signal, altering its capacity. To shed light into this matter, we show, among other results, how to calculate the information capacity of an active channel of communication. Then, we show that the channel capacity depends on whether the active channel is self-excitable or not and that, contrary to a current belief, desynchronization can provide an environment in which large amounts of information can be transmitted in a channel that is self-excitable. An interesting case of a self-excitable active channel is a network of electrically connected Hindmarsh-Rose chaotic neurons.
A novel approach to characterize information radiation in complex networks
NASA Astrophysics Data System (ADS)
Wang, Xiaoyang; Wang, Ying; Zhu, Lin; Li, Chao
2016-06-01
The traditional research of information dissemination is mostly based on the virus spreading model that the information is being spread by probability, which does not match very well to the reality, because the information that we receive is always more or less than what was sent. In order to quantitatively describe variations in the amount of information during the spreading process, this article proposes a safety information radiation model on the basis of communication theory, combining with relevant theories of complex networks. This model comprehensively considers the various influence factors when safety information radiates in the network, and introduces some concepts from the communication theory perspective, such as the radiation gain function, receiving gain function, information retaining capacity and information second reception capacity, to describe the safety information radiation process between nodes and dynamically investigate the states of network nodes. On a micro level, this article analyzes the influence of various initial conditions and parameters on safety information radiation through the new model simulation. The simulation reveals that this novel approach can reflect the variation of safety information quantity of each node in the complex network, and the scale-free network has better ;radiation explosive power;, while the small-world network has better ;radiation staying power;. The results also show that it is efficient to improve the overall performance of network security by selecting nodes with high degrees as the information source, refining and simplifying the information, increasing the information second reception capacity and decreasing the noises. In a word, this article lays the foundation for further research on the interactions of information and energy between internal components within complex systems.
Time series characterization via horizontal visibility graph and Information Theory
NASA Astrophysics Data System (ADS)
Gonçalves, Bruna Amin; Carpi, Laura; Rosso, Osvaldo A.; Ravetti, Martín G.
2016-12-01
Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degree distribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.
Effective information spreading based on local information in correlated networks
NASA Astrophysics Data System (ADS)
Gao, Lei; Wang, Wei; Pan, Liming; Tang, Ming; Zhang, Hai-Feng
2016-12-01
Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and a moderate correlation coefficient results in the most efficient information spreading. Incorporating the local informed density information into contact strategy, the convergence time of information spreading can be further reduced, and be minimized by an moderately preferential selection.
Research into Queueing Network Theory.
1977-09-01
aspects. Kleinrock [33] comments on this result, for example. In 1976, Burke [6] provided the first proof of some of what was occurring in the flow...server first passes through other servers (as for example in Jackson networks with loops) that item is delayed on its return. All current knowledge about...system simplification. A simplification is an operation on the system such that a new system is obtained subject to two requirements. First , the
An introductory review of information theory in the context of computational neuroscience.
McDonnell, Mark D; Ikeda, Shiro; Manton, Jonathan H
2011-07-01
This article introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience; information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.
Broadcasting Topology and Routing Information in Computer Networks
1985-05-01
Andrew S. Tanenbaum , Computer Networks. Prentice-Hall, Inc., 1981. [21 Frank Harvey, Graph Theory. Addison-Wesley, 1969. p [31 J. M. McQuillan and D...networks is that of keeping all nodes informed of the current operational status of each communication link in the network. The failure or repair of one or...where the network topology is in a nearly continuous state of change. 8 • I 1.2 The Topology Problem At any time while a network is in operation , one of
Operationalizing Network Theory for Ecosystem Service Assessments.
Dee, Laura E; Allesina, Stefano; Bonn, Aletta; Eklöf, Anna; Gaines, Steven D; Hines, Jes; Jacob, Ute; McDonald-Madden, Eve; Possingham, Hugh; Schröter, Matthias; Thompson, Ross M
2017-02-01
Managing ecosystems to provide ecosystem services in the face of global change is a pressing challenge for policy and science. Predicting how alternative management actions and changing future conditions will alter services is complicated by interactions among components in ecological and socioeconomic systems. Failure to understand those interactions can lead to detrimental outcomes from management decisions. Network theory that integrates ecological and socioeconomic systems may provide a path to meeting this challenge. While network theory offers promising approaches to examine ecosystem services, few studies have identified how to operationalize networks for managing and assessing diverse ecosystem services. We propose a framework for how to use networks to assess how drivers and management actions will directly and indirectly alter ecosystem services.
Towards a predictive theory for genetic regulatory networks
NASA Astrophysics Data System (ADS)
Tkacik, Gasper
When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.
An information theory framework for dynamic functional domain connectivity.
Vergara, Victor M; Miller, Robyn; Calhoun, Vince
2017-06-01
Dynamic functional network connectivity (dFNC) analyzes time evolution of coherent activity in the brain. In this technique dynamic changes are considered for the whole brain. This paper proposes an information theory framework to measure information flowing among subsets of functional networks call functional domains. Our method aims at estimating bits of information contained and shared among domains. The succession of dynamic functional states is estimated at the domain level. Information quantity is based on the probabilities of observing each dynamic state. Mutual information measurement is then obtained from probabilities across domains. Thus, we named this value the cross domain mutual information (CDMI). Strong CDMIs were observed in relation to the subcortical domain. Domains related to sensorial input, motor control and cerebellum form another CDMI cluster. Information flow among other domains was seldom found. Other methods of dynamic connectivity focus on whole brain dFNC matrices. In the current framework, information theory is applied to states estimated from pairs of multi-network functional domains. In this context, we apply information theory to measure information flow across functional domains. Identified CDMI clusters point to known information pathways in the basal ganglia and also among areas of sensorial input, patterns found in static functional connectivity. In contrast, CDMI across brain areas of higher level cognitive processing follow a different pattern that indicates scarce information sharing. These findings show that employing information theory to formally measured information flow through brain domains reveals additional features of functional connectivity. Copyright © 2017 Elsevier B.V. All rights reserved.
Linear control theory for gene network modeling.
Shin, Yong-Jun; Bleris, Leonidas
2010-09-16
Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.
Local Area Networks for Information Retrieval.
ERIC Educational Resources Information Center
Kibirige, Harry M.
This examination of the use of local area networks (LANs) by libraries summarizes the findings of a nationwide survey of 600 libraries and information centers and 200 microcomputer networking system manufacturers and vendors, which was conducted to determine the relevance of currently available networking systems for library and information center…
Computer-Based Information Networks: Selected Examples.
ERIC Educational Resources Information Center
Hardesty, Larry
The history, purpose, and operation of six computer-based information networks are described in general and nontechnical terms. In the introduction the many definitions of an information network are explored. Ohio College Library Center's network (OCLC) is the first example. OCLC began in 1963, and since early 1973 has been extending its services…
Computer-Based Information Networks: Selected Examples.
ERIC Educational Resources Information Center
Hardesty, Larry
The history, purpose, and operation of six computer-based information networks are described in general and nontechnical terms. In the introduction the many definitions of an information network are explored. Ohio College Library Center's network (OCLC) is the first example. OCLC began in 1963, and since early 1973 has been extending its services…
Exploring network theory for mass drug administration.
Chami, Goylette F; Molyneux, David H; Kontoleon, Andreas A; Dunne, David W
2013-08-01
Network theory is a well-established discipline that uses mathematical graphs to describe biological, physical, and social systems. The topologies across empirical networks display strikingly similar organizational properties. In particular, the characteristics of these networks allow computational analysis to contribute data unattainable from examining individual components in isolation. However, the interdisciplinary and quantitative nature of network analysis has yet to be exploited by public health initiatives to distribute preventive chemotherapies. One notable application is the 2012 World Health Organization (WHO) Roadmap for Neglected Tropical Diseases (NTDs) where there is a need to upscale distribution capacity and to target systematic noncompliers. An understanding of local networks for analysing the distributional properties of community-directed treatment may facilitate sustainable expansion of mass drug-administration (MDA) programs.
Information Theory for Information Science: Antecedents, Philosophy, and Applications
ERIC Educational Resources Information Center
Losee, Robert M.
2017-01-01
This paper provides an historical overview of the theoretical antecedents leading to information theory, specifically those useful for understanding and teaching information science and systems. Information may be discussed in a philosophical manner and at the same time be measureable. This notion of information can thus be the subject of…
Urban traffic-network performance: flow theory and simulation experiments
Williams, J.C.
1986-01-01
Performance models for urban street networks were developed to describe the response of a traffic network to given travel-demand levels. The three basic traffic flow variables, speed, flow, and concentration, are defined at the network level, and three model systems are proposed. Each system consists of a series of interrelated, consistent functions between the three basic traffic-flow variables as well as the fraction of stopped vehicles in the network. These models are subsequently compared with the results of microscopic simulation of a small test network. The sensitivity of one of the model systems to a variety of network features was also explored. Three categories of features were considered, with the specific features tested listed in parentheses: network topology (block length and street width), traffic control (traffic signal coordination), and traffic characteristics (level of inter-vehicular interaction). Finally, a fundamental issue concerning the estimation of two network-level parameters (from a nonlinear relation in the two-fluid theory) was examined. The principal concern was that of comparability of these parameters when estimated with information from a single vehicle (or small group of vehicles), as done in conjunction with previous field studies, and when estimated with network-level information (i.e., all the vehicles), as is possible with simulation.
Quantum mechanics and quantum information theory
NASA Astrophysics Data System (ADS)
van Camp, Wesley William
The principle aim of this dissertation is to investigate the philosophical application of quantum information theory to interpretational issues regarding the theory of quantum mechanics. Recently, quantum information theory has emerged as a potential source for such an interpretation. The main question with which this dissertation will be concerned is whether or not an information-theoretic interpretation can serve as a conceptually acceptable interpretation of quantum mechanics. It will be argued that some of the more obvious approaches -- that quantum information theory shows us that ultimately the world is made of information, and quantum Bayesianism -- fail as philosophical interpretations of quantum mechanics. However, the information-theoretic approach of Clifton, Bub, and Halvorson introduces Einstein's distinction between principle theories and constructive theories, arguing that quantum mechanics is best understood as an information-theoretic principle theory. While I argue that this particular approach fails, it does offer a viable new philosophical role for information theory. Specifically, an investigation of interpretationally successful principle theories such as Newtonian mechanics, special relativity, and general relativity, shows that the particular principles employed are necessary as constitutive elements of a framework which partially defines the basic explanatory concepts of space, time, and motion. Without such constitutive principles as preconditions for empirical meaning, scientific progress is hampered. It is argued that the philosophical issues in quantum mechanics stem from an analogous conceptual crisis. On the basis of this comparison, the best strategy for resolving these problems is to apply a similar sort of conceptual analysis to quantum mechanics so as to provide an appropriate set of constitutive principles clarifying the conceptual issues at stake. It is further argued that quantum information theory is ideally placed as a novel
Dynamic information routing in complex networks
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2016-01-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257
Dynamic information routing in complex networks
NASA Astrophysics Data System (ADS)
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2016-04-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.
Information Processing Theory: Classroom Applications.
ERIC Educational Resources Information Center
Slate, John R.; Charlesworth, John R., Jr.
The information processing model, a theoretical framework of how humans think, reason, and learn, views human cognitive functioning as analogous to the operation of a computer. This paper uses the increased understanding of the information processing model to provide teachers with suggestions for improving the teaching-learning process. Major…
Hodge Decomposition of Information Flow on Small-World Networks.
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.
Hodge Decomposition of Information Flow on Small-World Networks
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow. PMID:27733817
Nonequilibrium landscape theory of neural networks.
Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin
2013-11-05
The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.
Nonequilibrium landscape theory of neural networks
Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin
2013-01-01
The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451
Maximizing information exchange between complex networks
NASA Astrophysics Data System (ADS)
West, Bruce J.; Geneston, Elvis L.; Grigolini, Paolo
2008-10-01
Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically the scientific study of any given complex phenomenon generates a number of explanations, from a variety of perspectives, that eventually requires synthesis to achieve a deep level of insight and understanding. One such synthesis has created the field of out-of-equilibrium statistical physics as applied to the understanding of complex dynamic networks. Over the past forty years the concept of complexity has undergone a metamorphosis. Complexity was originally seen as a consequence of memory in individual particle trajectories, in full agreement with a Hamiltonian picture of microscopic dynamics and, in principle, macroscopic dynamics could be derived from the microscopic Hamiltonian picture. The main difficulty in deriving macroscopic dynamics from microscopic dynamics is the need to take into account the actions of a very large number of components. The existence of events such as abrupt jumps, considered by the conventional continuous time random walk approach to describing complexity was never perceived as conflicting with the Hamiltonian view. Herein we review many of the reasons why this traditional Hamiltonian view of complexity is unsatisfactory. We show that as a result of technological advances, which make the observation of single elementary events possible, the definition of complexity has shifted from the conventional memory concept towards the action of non-Poisson renewal events. We show that the observation of crucial processes, such as the intermittent fluorescence of blinking quantum dots as well as the brain’s response to music, as monitored by a set of electrodes attached to the scalp, has forced investigators to go beyond the traditional concept of complexity and to establish closer contact with the nascent field of complex networks. Complex networks form one of the most challenging areas of
Multimedia Information Networks in Social Media
NASA Astrophysics Data System (ADS)
Cao, Liangliang; Qi, Guojun; Tsai, Shen-Fu; Tsai, Min-Hsuan; Pozo, Andrey Del; Huang, Thomas S.; Zhang, Xuemei; Lim, Suk Hwan
The popularity of personal digital cameras and online photo/video sharing community has lead to an explosion of multimedia information. Unlike traditional multimedia data, many new multimedia datasets are organized in a structural way, incorporating rich information such as semantic ontology, social interaction, community media, geographical maps, in addition to the multimedia contents by themselves. Studies of such structured multimedia data have resulted in a new research area, which is referred to as Multimedia Information Networks. Multimedia information networks are closely related to social networks, but especially focus on understanding the topics and semantics of the multimedia files in the context of network structure. This chapter reviews different categories of recent systems related to multimedia information networks, summarizes the popular inference methods used in recent works, and discusses the applications related to multimedia information networks. We also discuss a wide range of topics including public datasets, related industrial systems, and potential future research directions in this field.
Information transduction capacity of noisy biochemical signaling networks
Cheong, Raymond; Rhee, Alex; Wang, Chiaochun Joanne; Nemenman, Ilya; Levchenko, Andre
2014-01-01
Molecular noise restricts the ability of an individual cell to resolve input signals of different strengths and gather information about the external environment. Transmitting information through complex signaling networks with redundancies can overcome this limitation. We developed an integrative theoretical and experimental framework, based on the formalism of information theory, to quantitatively predict and measure the amount of information transduced by molecular and cellular networks. Analyzing tumor necrosis factor (TNF) signaling revealed that individual TNF signaling pathways transduce information sufficient for accurate binary decisions, and an upstream bottleneck limits the information gained via multiple pathways together. Negative feedback to this bottleneck could both alleviate and enhance its limiting effect, despite decreasing noise. Bottlenecks likewise constrain information attained by networks signaling through multiple genes or cells. PMID:21921160
A security architecture for health information networks.
Kailar, Rajashekar; Muralidhar, Vinod
2007-10-11
Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.
Bridging genetic networks and queueing theory
NASA Astrophysics Data System (ADS)
Arazi, Arnon; Ben-Jacob, Eshel; Yechiali, Uri
2004-02-01
One of the main challenges facing biology today is the understanding of the joint action of genes, proteins and RNA molecules, interwoven in intricate interdependencies commonly known as genetic networks. To this end, several mathematical approaches have been introduced to date. In addition to developing the analytical tools required for this task anew, one can utilize knowledge found in existing disciplines, specializing in the representation and analysis of systems featuring similar aspects. We suggest queueing theory as a possible source of such knowledge. This discipline, which focuses on the study of workloads forming in a variety of scenarios, offers an assortment of tools allowing for the derivation of the statistical properties of the inspected systems. We argue that a proper adaptation of modeling techniques and analytical methods used in queueing theory can contribute to the study of genetic regulatory networks. This is demonstrated by presenting a queueing-inspired model of a genetic network of arbitrary size and structure, for which the probability distribution function is derived. This model is further applied to the description of the lac operon regulation mechanism. In addition, we discuss the possible benefits stemming for queueing theory from the interdisciplinary dialogue with molecular biology-in particular, the incorporation of various dynamical behaviours into queueing networks.
Econophysics: from Game Theory and Information Theory to Quantum Mechanics
NASA Astrophysics Data System (ADS)
Jimenez, Edward; Moya, Douglas
2005-03-01
Rationality is the universal invariant among human behavior, universe physical laws and ordered and complex biological systems. Econophysics isboth the use of physical concepts in Finance and Economics, and the use of Information Economics in Physics. In special, we will show that it is possible to obtain the Quantum Mechanics principles using Information and Game Theory.
Modeling information flow in biological networks.
Kim, Yoo-Ah; Przytycki, Jozef H; Wuchty, Stefan; Przytycka, Teresa M
2011-06-01
Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.
Towards understanding the behavior of physical systems using information theory
NASA Astrophysics Data System (ADS)
Quax, Rick; Apolloni, Andrea; Sloot, Peter M. A.
2013-09-01
One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.
How Can Functional Neuroimaging Inform Cognitive Theories?
Coltheart, Max
2013-01-01
Work on functional neuroimaging of cognition falls into two categories. The first aims at localizing specific cognitive subsystems in specific brain regions. In this research, the cognitive subsystems in question need to be defined independently of the neuroimaging data because the interpretation of the data requires such definition; so functional neuroimaging is informed by cognitive theories rather than informing them. The second category uses neuroimaging data to test cognitive theories. As cognitive theories are expressed in cognitive terms, such theories have to be embellished by explicit proposals about relationships between cognition and the brain if they are to become capable of generating predictions about the results of experiments that use functional neuroimaging. Whether functional neuroimaging can succeed in informing a cognitive theory depends critically upon the plausibility of such supplementary proposals. It is also critical to avoid the "consistency fallacy." When neuroimaging data from an experiment are consistent with predictions from a particular cognitive theory, this cannot be offered as evidence in support of that theory unless it can be shown that there were possible other outcomes of the experiment that are inconsistent with the theory-outcomes that would have falsified predictions from the theory had they been obtained. © The Author(s) 2013.
Information Theory and the Earth's Density Distribution
NASA Technical Reports Server (NTRS)
Rubincam, D. P.
1979-01-01
An argument for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia lead to a density distribution which was surprisingly close to the optimum distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.
Information theory and the earth's density distribution
NASA Technical Reports Server (NTRS)
Rubincam, D. P.
1978-01-01
The present paper argues for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia leads to a density distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.
Optimizing information flow in biological networks
NASA Astrophysics Data System (ADS)
Bialek, William
2009-03-01
The generation of physicists who turned to the phenomena of life in the 1930s realized that to understand these phenomena one would need to track not just the flow of energy (as in inanimate systems) but also the flow of information. It would take more than a decade before Shannon provided the tools to formalize this intuition, making precise the connection between entropy and information. Since Shannon, many investigators have explored the possibility that biological mechanisms are selected to maximize the efficiency with which information is transmitted or represented, subject to fundamental physical constraints. I will survey these efforts, emphasizing that the same principles are being used in thinking about biological systems at very different levels of organization, from bacteria to brains. Although sometimes submerged under concerns about particular systems, the idea that information flow is optimized provides us with a candidate for a real theory of biological networks, rather than just a collection of parameterized models. I will try to explain why I think the time is right to focus on this grand theoretical goal, pointing to some key open problems and opportunities for connection to emerging experiments.
Predicting Information Flows in Network Traffic.
ERIC Educational Resources Information Center
Hinich, Melvin J.; Molyneux, Robert E.
2003-01-01
Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)
Predicting Information Flows in Network Traffic.
ERIC Educational Resources Information Center
Hinich, Melvin J.; Molyneux, Robert E.
2003-01-01
Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)
Exploring network operations for data and information networks
NASA Astrophysics Data System (ADS)
Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming
2017-01-01
Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.
Information Assurance in Sensor Networks
2009-09-15
Chen, “Imorl: Incremental multiple-object recognition and localization,” IEEE Trans. Neural Networks , vol. 19, no. 10, pp. 1727–1738, 2008. [50] H...according to specific requirements, the cost-sensitive neural networks proposed in [25] that produce learning algorithms with powerful applicative...incremental learning fuzzy neural (ILFN) network for fault detection and classification in a machinery condition or health monitoring environment [38
Protein contact prediction by using information theory
NASA Astrophysics Data System (ADS)
Byeon, Jae-Young; Lee, Julian
2017-05-01
We develop a novel method for predicting the inter-residue contacts of a protein from evolutionary information obtained from the alignment of multiple sequences. Our method is based on information theory, where we use conditional mutual information so that the spurious correlations coming from indirect effects are removed. The benchmark test shows better performance than the previous method using mutual information does, suggesting the potential of the new method.
A Free Object in Quantum Information Theory
2010-01-01
process of teleporting quantum information with a given entangled state. The third is purely a mathematical construction, the free affine monoid over the...Klein four group. We prove that all three of these objects are isomorphic. Keywords: Information Theory, Quantum Channel, Category, Teleportation ...information theoretic properties are easy to calculate. What are their higher dimensional analogues? (iv) If we attempt to teleport quantum information
Mutual information in a dilute, asymmetric neural network model
NASA Astrophysics Data System (ADS)
Greenfield, Elliot
We study the computational properties of a neural network consisting of binary neurons with dilute asymmetric synaptic connections. This simple model allows us to simulate large networks which can reflect more of the architecture and dynamics of real neural networks. Our main goal is to determine the dynamical behavior that maximizes the network's ability to perform computations. To this end, we apply information theory, measuring the average mutual information between pairs of pre- and post-synaptic neurons. Communication of information between neurons is an essential requirement for collective computation. Previous workers have demonstrated that neural networks with asymmetric connections undergo a transition from ordered to chaotic behavior as certain network parameters, such as the connectivity, are changed. We find that the average mutual information has a peak near the order-chaos transition, implying that the network can most efficiently communicate information between cells in this region. The mutual information peak becomes increasingly pronounced when the basic model is extended to incorporate more biologically realistic features, such as a variable threshold and nonlinear summation of inputs. We find that the peak in mutual information near the phase transition is a robust feature of the system for a wide range of assumptions about post-synaptic integration.
Information jet: Handling noisy big data from weakly disconnected network
NASA Astrophysics Data System (ADS)
Aurongzeb, Deeder
Sudden aggregation (information jet) of large amount of data is ubiquitous around connected social networks, driven by sudden interacting and non-interacting events, network security threat attacks, online sales channel etc. Clustering of information jet based on time series analysis and graph theory is not new but little work is done to connect them with particle jet statistics. We show pre-clustering based on context can element soft network or network of information which is critical to minimize time to calculate results from noisy big data. We show difference between, stochastic gradient boosting and time series-graph clustering. For disconnected higher dimensional information jet, we use Kallenberg representation theorem (Kallenberg, 2005, arXiv:1401.1137) to identify and eliminate jet similarities from dense or sparse graph.
The Social Side of Information Networking.
ERIC Educational Resources Information Center
Katz, James E.
1997-01-01
Explores the social issues, including manners, security, crime (fraud), and social control associated with information networking, with emphasis on the Internet. Also addresses the influence of cellular phones, the Internet and other information technologies on society. (GR)
Network Theory Tools for RNA Modeling
Kim, Namhee; Petingi, Louis; Schlick, Tamar
2014-01-01
An introduction into the usage of graph or network theory tools for the study of RNA molecules is presented. By using vertices and edges to define RNA secondary structures as tree and dual graphs, we can enumerate, predict, and design RNA topologies. Graph connectivity and associated Laplacian eigenvalues relate to biological properties of RNA and help understand RNA motifs as well as build, by computational design, various RNA target structures. Importantly, graph theoretical representations of RNAs reduce drastically the conformational space size and therefore simplify modeling and prediction tasks. Ongoing challenges remain regarding general RNA design, representation of RNA pseudoknots, and tertiary structure prediction. Thus, developments in network theory may help advance RNA biology. PMID:25414570
Network Theory Tools for RNA Modeling.
Kim, Namhee; Petingi, Louis; Schlick, Tamar
2013-09-01
An introduction into the usage of graph or network theory tools for the study of RNA molecules is presented. By using vertices and edges to define RNA secondary structures as tree and dual graphs, we can enumerate, predict, and design RNA topologies. Graph connectivity and associated Laplacian eigenvalues relate to biological properties of RNA and help understand RNA motifs as well as build, by computational design, various RNA target structures. Importantly, graph theoretical representations of RNAs reduce drastically the conformational space size and therefore simplify modeling and prediction tasks. Ongoing challenges remain regarding general RNA design, representation of RNA pseudoknots, and tertiary structure prediction. Thus, developments in network theory may help advance RNA biology.
A Security Architecture for Health Information Networks
Kailar, Rajashekar
2007-01-01
Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862
Post Disaster Governance, Complexity and Network Theory
Lassa, Jonatan A.
2015-01-01
This research aims to understand the organizational network typology of large-scale disaster intervention in developing countries and to understand the complexity of post-disaster intervention, through the use of network theory based on empirical data from post-tsunami reconstruction in Aceh, Indonesia, during 2005/2007. The findings suggest that the ‘ degrees of separation’ (or network diameter) between any two organizations in the field is 5, thus reflecting ‘small world’ realities and therefore making no significant difference with the real human networks, as found in previous experiments. There are also significant loops in the network reflecting the fact that some actors tend to not cooperate, which challenges post disaster coordination. The findings show the landscape of humanitarian actors is not randomly distributed. Many actors were connected to each other through certain hubs, while hundreds of actors make ‘scattered’ single ‘principal-client’ links. The paper concludes that by understanding the distribution of degree, centrality, ‘degrees of separation’ and visualization of the network, authorities can improve their understanding of the realities of coordination, from macro to micro scales. PMID:26236562
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher
2005-01-01
This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.
Basing quantum theory on information processing
NASA Astrophysics Data System (ADS)
Barnum, Howard
2008-03-01
I consider information-based derivations of the quantum formalism, in a framework encompassing quantum and classical theory and a broad spectrum of theories serving as foils to them. The most ambitious hope for such a derivation is a role analogous to Einstein's development of the dynamics and kinetics of macroscopic bodies, and later of their gravitational interactions, on the basis of simple principles with clear operational meanings and experimental consequences. Short of this, it could still provide a principled understanding of the features of quantum mechanics that account for its greater-than-classical information-processing power, helping guide the search for new quantum algorithms and protocols. I summarize the convex operational framework for theories, and discuss information-processing in theories therein. Results include the fact that information that can be obtained without disturbance is inherently classical, generalized no-cloning and no-broadcasting theorems, exponentially secure bit commitment in all non-classical theories without entanglement, properties of theories that allow teleportation, and properties of theories that allow ``remote steering'' of ensembles using entanglement. Joint work with collaborators including Jonathan Barrett, Matthew Leifer, Alexander Wilce, Oscar Dahlsten, and Ben Toner.
Garofalo, Matteo; Nieus, Thierry; Massobrio, Paolo; Martinoia, Sergio
2009-01-01
Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings. PMID:19652720
Conditioned reinforcement and information theory reconsidered.
Shahan, Timothy A; Cunningham, Paul
2015-03-01
The idea that stimuli might function as conditioned reinforcers because of the information they convey about primary reinforcers has a long history in the study of learning. However, formal application of information theory to conditioned reinforcement has been largely abandoned in modern theorizing because of its failures with respect to observing behavior. In this paper we show how recent advances in the application of information theory to Pavlovian conditioning offer a novel approach to conditioned reinforcement. The critical feature of this approach is that calculations of information are based on reductions of uncertainty about expected time to primary reinforcement signaled by a conditioned reinforcer. Using this approach, we show that previous failures of information theory with observing behavior can be remedied, and that the resulting framework produces predictions similar to Delay Reduction Theory in both observing-response and concurrent-chains procedures. We suggest that the similarity of these predictions might offer an analytically grounded reason for why Delay Reduction Theory has been a successful theory of conditioned reinforcement. Finally, we suggest that the approach provides a formal basis for the assertion that conditioned reinforcement results from Pavlovian conditioning and may provide an integrative approach encompassing both domains. © Society for the Experimental Analysis of Behavior.
Using graph theory to analyze biological networks
2011-01-01
Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. PMID:21527005
Scientific Information Networks: A Case Study.
ERIC Educational Resources Information Center
Vallee, Jacques
The technical feasibility of a continental information network for astronomy has been demonstrated in the course of a two-month experiment conducted jointly by Dearborn Observatory of Northwestern University and the Stanford University Computation Center. The experiment simulated a scientific information network based on a high-level retrieval…
Information Services in the International Network Marketplace.
ERIC Educational Resources Information Center
Hepworth, Mark E.
1987-01-01
Examines the internationalism of the network marketplace through case studies of the London Stock Exchange and I. P. Sharp Associates, a Canadian computer service bureau. Discussion focuses on the importance of transnational computer networks to the production of information services and marketplace expansion, and global information policy issues.…
Information Processing Theory and Conceptual Development.
ERIC Educational Resources Information Center
Schroder, H. M.
An educational program based upon information processing theory has been developed at Southern Illinois University. The integrating theme was the development of conceptual ability for coping with social and personal problems. It utilized student information search and concept formation as foundations for discussion and judgment and was organized…
Network theory and its applications in economic systems
NASA Astrophysics Data System (ADS)
Huang, Xuqing
This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. 2) We analytically demonstrates that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the
ERIC Educational Resources Information Center
Cheverie, Joan F.
1999-01-01
Discusses the development of strategies for providing access to and services for U.S. federal government information in higher education using the global information infrastructure, from the perspective of the Coalition for Networked Information (CNI). Discusses the preservation of electronic information and networked information discovery and…
The application of information theory to biochemical signaling systems
Rhee, Alex; Cheong, Raymond; Levchenko, Andre
2012-01-01
Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell’s signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon’s information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling. PMID:22872091
Quantum networks: General theory and applications
NASA Astrophysics Data System (ADS)
Bisio, A.; Chiribella, G.; D'Ariano, G. M.; Perinotti, P.
2011-06-01
In this work we present a general mathematical framework to deal with
Information theory based approaches to cellular signaling.
Waltermann, Christian; Klipp, Edda
2011-10-01
Cells interact with their environment and they have to react adequately to internal and external changes such changes in nutrient composition, physical properties like temperature or osmolarity and other stresses. More specifically, they must be able to evaluate whether the external change is significant or just in the range of noise. Based on multiple external parameters they have to compute an optimal response. Cellular signaling pathways are considered as the major means of information perception and transmission in cells. Here, we review different attempts to quantify information processing on the level of individual cells. We refer to Shannon entropy, mutual information, and informal measures of signaling pathway cross-talk and specificity. Information theory in systems biology has been successfully applied to identification of optimal pathway structures, mutual information and entropy as system response in sensitivity analysis, and quantification of input and output information. While the study of information transmission within the framework of information theory in technical systems is an advanced field with high impact in engineering and telecommunication, its application to biological objects and processes is still restricted to specific fields such as neuroscience, structural and molecular biology. However, in systems biology dealing with a holistic understanding of biochemical systems and cellular signaling only recently a number of examples for the application of information theory have emerged. This article is part of a Special Issue entitled Systems Biology of Microorganisms. Copyright © 2011 Elsevier B.V. All rights reserved.
Network meta-analysis, electrical networks and graph theory.
Rücker, Gerta
2012-12-01
Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.
Theory of correlations in stochastic neural networks
NASA Astrophysics Data System (ADS)
Ginzburg, Iris; Sompolinsky, Haim
1994-10-01
One of the main experimental tools in probing the interactions between neurons has been the measurement of the correlations in their activity. In general, however, the interpretation of the observed correlations is difficult since the correlation between a pair of neurons is influenced not only by the direct interaction between them but also by the dynamic state of the entire network to which they belong. Thus a comparison between the observed correlations and the predictions from specific model networks is needed. In this paper we develop a theory of neuronal correlation functions in large networks comprising several highly connected subpopulations and obeying stochastic dynamic rules. When the networks are in asynchronous states, the cross correlations are relatively weak, i.e., their amplitude relative to that of the autocorrelations is of order of 1/N, N being the size of the interacting populations. Using the weakness of the cross correlations, general equations that express the matrix of cross correlations in terms of the mean neuronal activities and the effective interaction matrix are presented. The effective interactions are the synaptic efficacies multiplied by the gain of the postsynaptic neurons. The time-delayed cross-correlation matrix can be expressed as a sum of exponentially decaying modes that correspond to the (nonorthogonal) eigenvectors of the effective interaction matrix. The theory is extended to networks with random connectivity, such as randomly dilute networks. This allows for a comparison between the contribution from the internal common input and that from the direct interactions to the correlations of monosynaptically coupled pairs. A closely related quantity is the linear response of the neurons to external time-dependent perturbations. We derive the form of the dynamic linear response function of neurons in the above architecture in terms of the eigenmodes of the effective interaction matrix. The behavior of the correlations and the
Effective information spreading based on local information in correlated networks
Gao, Lei; Wang, Wei; Pan, Liming; Tang, Ming; Zhang, Hai-Feng
2016-01-01
Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and a moderate correlation coefficient results in the most efficient information spreading. Incorporating the local informed density information into contact strategy, the convergence time of information spreading can be further reduced, and be minimized by an moderately preferential selection. PMID:27910882
Noise enhances information transfer in hierarchical networks.
Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A
2013-01-01
We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.
Noise enhances information transfer in hierarchical networks
Czaplicka, Agnieszka; Holyst, Janusz A.; Sloot, Peter M. A.
2013-01-01
We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574
Reaction networks and evolutionary game theory.
Veloz, Tomas; Razeto-Barry, Pablo; Dittrich, Peter; Fajardo, Alejandro
2014-01-01
The powerful mathematical tools developed for the study of large scale reaction networks have given rise to applications of this framework beyond the scope of biochemistry. Recently, reaction networks have been suggested as an alternative way to model social phenomena. In this "socio-chemical metaphor" molecular species play the role of agents' decisions and their outcomes, and chemical reactions play the role of interactions among these decisions. From here, it is possible to study the dynamical properties of social systems using standard tools of biochemical modelling. In this work we show how to use reaction networks to model systems that are usually studied via evolutionary game theory. We first illustrate our framework by modeling the repeated prisoners' dilemma. The model is built from the payoff matrix together with assumptions of the agents' memory and recognizability capacities. The model provides consistent results concerning the performance of the agents, and allows for the examination of the steady states of the system in a simple manner. We further develop a model considering the interaction among Tit for Tat and Defector agents. We produce analytical results concerning the performance of the strategies in different situations of agents' memory and recognizability. This approach unites two important theories and may produce new insights in classical problems such as the evolution of cooperation in large scale systems.
Dynamic Sampling and Information Encoding in Biochemical Networks
NASA Astrophysics Data System (ADS)
Potter, Garrett D.; Byrd, Tommy A.; Mugler, Andrew; Sun, Bo
2017-02-01
Cells use biochemical networks to translate environmental information into intracellular responses. These responses can be highly dynamic, but how the information is encoded in these dynamics remains poorly understood. Here we investigate the dynamic encoding of information in the ATP-induced calcium responses of fibroblast cells, using a vectorial, or multi-time-point, measure from information theory. We find that the amount of extracted information depends on physiological constraints such as the sampling rate and memory capacity of the downstream network, and is affected differentially by intrinsic vs. extrinsic noise. By comparing to a minimal physical model, we find, surprisingly, that the information is often insensitive to the detailed structure of the underlying dynamics, and instead the decoding mechanism acts as a simple low-pass filter. These results demonstrate the mechanisms and limitations of dynamic information storage in cells.
Pathways, Networks, and Systems: Theory and Experiments
Joseph H. Nadeau; John D. Lambris
2004-10-30
The international conference provided a unique opportunity for theoreticians and experimenters to exchange ideas, strategies, problems, challenges, language and opportunities in both formal and informal settings. This dialog is an important step towards developing a deep and effective integration of theory and experiments in studies of systems biology in humans and model organisms.
Reasonable fermionic quantum information theories require relativity
NASA Astrophysics Data System (ADS)
Friis, Nicolai
2016-03-01
We show that any quantum information theory based on anticommuting operators must be supplemented by a superselection rule deeply rooted in relativity to establish a reasonable notion of entanglement. While quantum information may be encoded in the fermionic Fock space, the unrestricted theory has a peculiar feature: the marginals of bipartite pure states need not have identical entropies, which leads to an ambiguous definition of entanglement. We solve this problem, by proving that it is removed by relativity, i.e., by the parity superselection rule that arises from Lorentz invariance via the spin-statistics connection. Our results hence unveil a fundamental conceptual inseparability of quantum information and the causal structure of relativistic field theory.
Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice
ERIC Educational Resources Information Center
Transue, Beth M.
2013-01-01
Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…
Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice
ERIC Educational Resources Information Center
Transue, Beth M.
2013-01-01
Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…
Information flow analysis of interactome networks.
Missiuro, Patrycja Vasilyev; Liu, Kesheng; Zou, Lihua; Ross, Brian C; Zhao, Guoyan; Liu, Jun S; Ge, Hui
2009-04-01
Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression
Hierarchical social networks and information flow
NASA Astrophysics Data System (ADS)
López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.
2002-12-01
Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.
Information theory, spectral geometry, and quantum gravity.
Kempf, Achim; Martin, Robert
2008-01-18
We show that there exists a deep link between the two disciplines of information theory and spectral geometry. This allows us to obtain new results on a well-known quantum gravity motivated natural ultraviolet cutoff which describes an upper bound on the spatial density of information. Concretely, we show that, together with an infrared cutoff, this natural ultraviolet cutoff beautifully reduces the path integral of quantum field theory on curved space to a finite number of ordinary integrations. We then show, in particular, that the subsequent removal of the infrared cutoff is safe.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
the economics and computer science communities . Such a model of externality is motivated by several factors: • The physical effect of the number of...Information Dynamics in Networks: Models and Algorithms In this project, we investigated how network structure interplays with higher level processes in...online social networks. We investigated the appropriateness of existing mathematical models for explaining the structure of retweet cascades on
Informal networks: the company behind the chart.
Krackhardt, D; Hanson, J R
1993-01-01
A glance at an organizational chart can show who's the boss and who reports to whom. But this formal chart won't reveal which people confer on technical matters or discuss office politics over lunch. Much of the real work in any company gets done through this informal organization with its complex networks of relationships that cross functions and divisions. According to consultants David Krackhardt and Jeffrey Hanson, managers can harness the true power in their companies by diagramming three types of networks: the advice network, which reveals the people to whom others turn to get work done; the trust network, which uncovers who shares delicate information; and the communication network, which shows who talks about work-related matters. Using employee questionnaires, managers can generate network maps that will get to the root of many organizational problems. When a task force in a computer company, for example, was not achieving its goals, the CEO turned to network maps to find out why. He discovered that the task force leader was central in the advice network but marginal in the trust network. Task force members did not believe he would look out for their interests, so the CEO used the trust map to find someone to share responsibility for the group. And when a bank manager saw in the network map that there was little communication between tellers and supervisors, he looked for ways to foster interaction among employees of all levels. As companies continue to flatten and rely on teams, managers must rely less on their authority and more on understanding these informal networks. Managers who can use maps to identify, leverage, and revamp informal networks will have the key to success.
The Teen Health Information Network (THINK).
ERIC Educational Resources Information Center
Kuzel, Judith; Erickson, Su
1995-01-01
Discusses the Teen Health Information Network (THINK), a grant-funded partnership of Aurora, Illinois, public libraries, schools, and community agencies to provide materials, information, and programming on issues related to teen health. Seven appendixes provide detailed information on survey results, collection evaluation and development,…
The Teen Health Information Network (THINK).
ERIC Educational Resources Information Center
Kuzel, Judith; Erickson, Su
1995-01-01
Discusses the Teen Health Information Network (THINK), a grant-funded partnership of Aurora, Illinois, public libraries, schools, and community agencies to provide materials, information, and programming on issues related to teen health. Seven appendixes provide detailed information on survey results, collection evaluation and development,…
Neural networks as perpetual information generators
NASA Astrophysics Data System (ADS)
Englisch, Harald; Xiao, Yegao; Yao, Kailun
1991-07-01
The information gain in a neural network cannot be larger than the bit capacity of the synapses. It is shown that the equation derived by Engel et al. [Phys. Rev. A 42, 4998 (1990)] for the strongly diluted network with persistent stimuli contradicts this condition. Furthermore, for any time step the correct equation is derived by taking the correlation between random variables into account.
Spinal Cord Injury Model System Information Network
... for Women with SCI Video Series EatRight® Weight Management Program Smoking's Effects on ... of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network as a ...
Equity trees and graphs via information theory
NASA Astrophysics Data System (ADS)
Harré, M.; Bossomaier, T.
2010-01-01
We investigate the similarities and differences between two measures of the relationship between equities traded in financial markets. Our measures are the correlation coefficients and the mutual information. In the context of financial markets correlation coefficients are well established whereas mutual information has not previously been as well studied despite its theoretically appealing properties. We show that asset trees which are derived from either the correlation coefficients or the mutual information have a mixture of both similarities and differences at the individual equity level and at the macroscopic level. We then extend our consideration from trees to graphs using the "genus 0" condition recently introduced in order to study the networks of equities.
Curriculum Theory and Library and Information Science.
ERIC Educational Resources Information Center
McGarry, Kevin J.
1987-01-01
Discusses curriculum theory in the context of professional education for library and information science, and outlines steps necessary for curriculum planning: (1) diagnosis of needs; (2) formulation of objectives; (3) selection of content from the universe of knowledge; (4) selection of learning experience; (5) organization of learning materials;…
Engaging Theories and Models to Inform Practice
ERIC Educational Resources Information Center
Kraus, Amanda
2012-01-01
Helping students prepare for the complex transition to life after graduation is an important responsibility shared by those in student affairs and others in higher education. This chapter explores theories and models that can inform student affairs practitioners and faculty in preparing students for life after college. The focus is on roles,…
Introduction to spiking neural networks: Information processing, learning and applications.
Ponulak, Filip; Kasinski, Andrzej
2011-01-01
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
Network thermodynamics and complexity: a transition to relational systems theory.
Mikulecky, D C
2001-07-01
Most systems of interest in today's world are highly structured and highly interactive. They cannot be reduced to simple components without losing a great deal of their system identity. Network thermodynamics is a marriage of classical and non-equilibrium thermodynamics along with network theory and kinetics to provide a practical framework for handling these systems. The ultimate result of any network thermodynamic model is still a set of state vector equations. But these equations are built in a new informative way so that information about the organization of the system is identifiable in the structure of the equations. The domain of network thermodynamics is all of physical systems theory. By using the powerful circuit simulator, the Simulation Program with Integrated Circuit Emphasis (SPICE), as a general systems simulator, any highly non-linear stiff system can be simulated. Furthermore, the theoretical findings of network thermodynamics are important new contributions. The contribution of a metric structure to thermodynamics compliments and goes beyond other recent work in this area. The application of topological reasoning through Tellegen's theorem shows that a mathematical structure exists into which all physical systems can be represented canonically. The old results in non-equilibrium thermodynamics due to Onsager can be reinterpreted and extended using these new, more holistic concepts about systems. Some examples are given. These are but a few of the many applications of network thermodynamics that have been proven to extend our capacity for handling the highly interactive, non-linear systems that populate both biology and chemistry. The presentation is carried out in the context of the recent growth of the field of complexity science. In particular, the context used for this discussion derives from the work of the mathematical biologist, Robert Rosen.
Stoichiometric network theory for nonequilibrium biochemical systems.
Qian, Hong; Beard, Daniel A; Liang, Shou-dan
2003-02-01
We introduce the basic concepts and develop a theory for nonequilibrium steady-state biochemical systems applicable to analyzing large-scale complex isothermal reaction networks. In terms of the stoichiometric matrix, we demonstrate both Kirchhoff's flux law sigma(l)J(l)=0 over a biochemical species, and potential law sigma(l) mu(l)=0 over a reaction loop. They reflect mass and energy conservation, respectively. For each reaction, its steady-state flux J can be decomposed into forward and backward one-way fluxes J = J+ - J-, with chemical potential difference deltamu = RT ln(J-/J+). The product -Jdeltamu gives the isothermal heat dissipation rate, which is necessarily non-negative according to the second law of thermodynamics. The stoichiometric network theory (SNT) embodies all of the relevant fundamental physics. Knowing J and deltamu of a biochemical reaction, a conductance can be computed which directly reflects the level of gene expression for the particular enzyme. For sufficiently small flux a linear relationship between J and deltamu can be established as the linear flux-force relation in irreversible thermodynamics, analogous to Ohm's law in electrical circuits.
Computational inference of neural information flow networks.
Smith, V Anne; Yu, Jing; Smulders, Tom V; Hartemink, Alexander J; Jarvis, Erich D
2006-11-24
Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.
Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.
Greenhalgh, Trisha; Stones, Rob
2010-05-01
The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes
Reverse Engineering Cellular Networks with Information Theoretic Methods
Villaverde, Alejandro F.; Ross, John; Banga, Julio R.
2013-01-01
Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets. PMID:24709703
78 FR 17418 - Rural Health Information Technology Network Development Grant
Federal Register 2010, 2011, 2012, 2013, 2014
2013-03-21
... HUMAN SERVICES Health Resources and Services Administration Rural Health Information Technology Network... award under the Rural Health Information Technology Network Development Grant (RHITND) to Grace... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network Development...
76 FR 67750 - Homeland Security Information Network Advisory Committee
Federal Register 2010, 2011, 2012, 2013, 2014
2011-11-02
... SECURITY Homeland Security Information Network Advisory Committee AGENCY: Department of Homeland Security... Applicants for Appointment to Homeland Security Information Network Advisory Committee. SUMMARY: The Secretary of Homeland Security has determined that the renewal of the Homeland Security Information Network...
Quantification of image quality using information theory.
Niimi, Takanaga; Maeda, Hisatoshi; Ikeda, Mitsuru; Imai, Kuniharu
2011-12-01
Aims of present study were to examine usefulness of information theory in visual assessment of image quality. We applied first order approximation of the Shannon's information theory to compute information losses (IL). Images of a contrast-detail mammography (CDMAM) phantom were acquired with computed radiographies for various radiation doses. Information content was defined as the entropy Σp( i )log(1/p ( i )), in which detection probabilities p ( i ) were calculated from distribution of detection rate of the CDMAM. IL was defined as the difference between information content and information obtained. IL decreased with increases in the disk diameters (P < 0.0001, ANOVA) and in the radiation doses (P < 0.002, F-test). Sums of IL, which we call total information losses (TIL), were closely correlated with the image quality figures (r = 0.985). TIL was dependent on the distribution of image reading ability of each examinee, even when average reading ratio was the same in the group. TIL was shown to be sensitive to the observers' distribution of image readings and was expected to improve the evaluation of image quality.
An information integration theory of consciousness
Tononi, Giulio
2004-01-01
Background Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition? Presentation of the hypothesis This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex. Testing the hypothesis The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As
An Information Theory of Hydrophobic Effects
NASA Astrophysics Data System (ADS)
Pratt, Lawrence R.
1998-03-01
The hydrophobic effect is a central concept in rationalizing the structure and stability of proteins in solution. However, a consensus has not been achieved on a molecular scale physical theory explaining the broad array of hydrophobic effects. Here we present an information theory designed to achieve consensus by identifying and limiting the physical information and assumptions sufficient to predict hydrophobic effects. The information theory is based upon the study of the probabilities of occupancy by water molecule centers of molecular scale volumes observed in neat liquid water. Predictions for hydrophobic effects can be extracted from this probability distribution. Simulation results show that this probability distribution is accurately predicted by a maximum entropy model using the two moments that are obtained from the experimental liquid density and the experimental radial distribution of oxygen atoms. We show the role of solvent molecule correlation functions of higher order than pairs. We show that this two moment model predicts known atomic scale hydrophobic effects: hydrophobic solubilities, potentials of mean force, and hydrophobic effects on conformational equilibria. We comment on the kinship between the two moment maximum entropy model and the earlier Pratt-Chandler theory of hydrophobic effects. We show that the model predicts the entropy convergence emphasized by high sensitivity calorimetry on the thermal denaturation of globular proteins and explains why this entropy convergence is insensitive to solute molecular details within the broad category of hydrophobic solutes. Finally, we consider the pressure denaturation of globular proteins and discuss the perspective that emerges from the information theory treatment: increasing pressure squeezes water molecules into the protein globule eventually separating hydrophobic components analogously to the separation of hydrophobic solutes in formation of clathrate hydrates.
Biological impacts and context of network theory
Almaas, E
2007-01-05
Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.
Fisheries Information Network in Indonesia.
ERIC Educational Resources Information Center
Balachandran, Sarojini
During the early 1980s the Indonesian government made a policy decision to develop fisheries as an important sector of the national economy. In doing so, it recognized the need for the collection and dissemination of fisheries research information not only for the scientists themselves, but also for the ultimate transfer of technology through…
Fisheries Information Network in Indonesia.
ERIC Educational Resources Information Center
Balachandran, Sarojini
During the early 1980s the Indonesian government made a policy decision to develop fisheries as an important sector of the national economy. In doing so, it recognized the need for the collection and dissemination of fisheries research information not only for the scientists themselves, but also for the ultimate transfer of technology through…
Brain parcellation based on information theory.
Bonmati, Ester; Bardera, Anton; Boada, Imma
2017-11-01
In computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions. The aim of this work is to present a robust whole-brain hierarchical parcellation that preserves the global structure of the network. Brain regions are modeled as a random walk on the connectome. From this model, a Markov process is derived, where the different nodes represent brain regions and in which the structure can be quantified. Functional or anatomical brain regions are clustered by using an agglomerative information bottleneck method that minimizes the overall loss of information of the structure by using mutual information as a similarity measure. The method is tested with synthetic models, structural and functional human connectomes and is compared with the classic k-means. Results show that the parcellated networks preserve the main properties and are consistent across subjects. This work provides a new framework to study the human connectome using functional or anatomical connectivity at different levels. Copyright © 2017 Elsevier B.V. All rights reserved.
Information Retrieval as a Network Application.
ERIC Educational Resources Information Center
Lynch, Clifford A.
1990-01-01
Describes the function of the Open Systems Interconnection (OSI) Z39.50 protocol, which allows for construction of information "servers"--i.e., resources attached to a computer communications network that can be accessed by client machines to retrieve information. The relationship of Z39.5 to other OSI protocols is explained. (23…
Distributing Executive Information Systems through Networks.
ERIC Educational Resources Information Center
Penrod, James I.; And Others
1993-01-01
Many colleges and universities will soon adopt distributed systems for executive information and decision support. Distribution of shared information through computer networks will improve decision-making processes dramatically on campuses. Critical success factors include administrative support, favorable organizational climate, ease of use,…
Distributing Executive Information Systems through Networks.
ERIC Educational Resources Information Center
Penrod, James I.; And Others
1993-01-01
Many colleges and universities will soon adopt distributed systems for executive information and decision support. Distribution of shared information through computer networks will improve decision-making processes dramatically on campuses. Critical success factors include administrative support, favorable organizational climate, ease of use,…
Child Rights Information Network Newsletter, 1997.
ERIC Educational Resources Information Center
Purbrick, Becky, Ed.
1997-01-01
These three newsletter issues communicate activities of the Child Rights Information Network (CRIN) and report on information resources and worldwide activities concerning children and child rights. The January 1997 issue profiles CRIN members in Costa Rica, Tanzania, Germany, and Switzerland; and provides updates on the activities of projects…
Protecting Personal Information on Social Networking Sites
ERIC Educational Resources Information Center
Gallant, David T.
2011-01-01
Almost everyone uses social networking sites like Facebook, MySpace, and LinkedIn. Since Facebook is the most popular site in the history of the Internet, this article will focus on how one can protect his/her personal information and how that extends to protecting the private information of others.
Searching LOGIN, the Local Government Information Network.
ERIC Educational Resources Information Center
Jack, Robert F.
1984-01-01
Describes a computer-based information retrieval and electronic messaging system produced by Control Data Corporation now being used by government agencies and other organizations. Background of Local Government Information Network (LOGIN), database structure, types of LOGIN units, searching LOGIN (intersect, display, and list commands), and how…
Ohio Valley Community Health Information Network.
ERIC Educational Resources Information Center
Guard, Roger; And Others
The Ohio Valley Community Health Information Network (OVCHIN) works to determine the efficacy of delivering health information to residents of rural southern Ohio and the urban and suburban Cincinnati area. OVCHIN is a community-based, consumer-defined demonstration grant program funded by the National Telecommunications and Information…
Child Rights Information Network Newsletter, 1996.
ERIC Educational Resources Information Center
Purbrick, Becky, Ed.
1996-01-01
These two newsletter issues communicate activities of the newly formed Child Rights Information Network (CRIN) and report on emerging information resources and activities concerning children and child rights. The January 1996 issue describes the history of CRIN, provides updates on the activities of projects linked to CRIN, and summarizes…
Information transfer in community structured multiplex networks
NASA Astrophysics Data System (ADS)
Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex
2015-08-01
The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.
Optimal information transfer in enzymatic networks: A field theoretic formulation
NASA Astrophysics Data System (ADS)
Samanta, Himadri S.; Hinczewski, Michael; Thirumalai, D.
2017-07-01
Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus [Hinczewski and Thirumalai, Phys. Rev. X 4, 041017 (2014), 10.1103/PhysRevX.4.041017]. We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudointermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudointermediate. Surprisingly, in these examples the minimum error computed using simulations that take nonlinearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second-order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in
Information theory in living systems, methods, applications, and challenges.
Gatenby, Robert A; Frieden, B Roy
2007-02-01
Living systems are distinguished in nature by their ability to maintain stable, ordered states far from equilibrium. This is despite constant buffeting by thermodynamic forces that, if unopposed, will inevitably increase disorder. Cells maintain a steep transmembrane entropy gradient by continuous application of information that permits cellular components to carry out highly specific tasks that import energy and export entropy. Thus, the study of information storage, flow and utilization is critical for understanding first principles that govern the dynamics of life. Initial biological applications of information theory (IT) used Shannon's methods to measure the information content in strings of monomers such as genes, RNA, and proteins. Recent work has used bioinformatic and dynamical systems to provide remarkable insights into the topology and dynamics of intracellular information networks. Novel applications of Fisher-, Shannon-, and Kullback-Leibler informations are promoting increased understanding of the mechanisms by which genetic information is converted to work and order. Insights into evolution may be gained by analysis of the the fitness contributions from specific segments of genetic information as well as the optimization process in which the fitness are constrained by the substrate cost for its storage and utilization. Recent IT applications have recognized the possible role of nontraditional information storage structures including lipids and ion gradients as well as information transmission by molecular flux across cell membranes. Many fascinating challenges remain, including defining the intercellular information dynamics of multicellular organisms and the role of disordered information storage and flow in disease.
Dynamic information theory and information description of dynamic systems
NASA Astrophysics Data System (ADS)
Xing, Xiusan
2010-04-01
In this paper, we develop dynamic statistical information theory established by the author. Starting from the ideas that the state variable evolution equations of stochastic dynamic systems, classical and quantum nonequilibrium statistical physical systems and special electromagnetic field systems can be regarded as their information symbol evolution equations and the definitions of dynamic information and dynamic entropy, we derive the evolution equations of dynamic information and dynamic entropy that describe the evolution laws of dynamic information. These four kinds of evolution equations are of the same mathematical type. They show in unison when information transmits in coordinate space outside the systems that the time rate of change of dynamic information densities originates from their drift, diffusion and dissipation in state variable space inside the systems and coordinate space in the transmission processes, and that the time rate of change of dynamic entropy densities is caused by their drift, diffusion and production in state variable space inside the systems and coordinate space in the transmission processes. When space noise can be neglected, an information wave will appear. If we only consider the information change inside the systems, dynamic information evolution equations reduce to information equations corresponding to the dynamic equations which describe evolution laws of the above dynamic systems. This reveals that the evolution laws of respective dynamic systems can be described by information equations in a unified fashion. Hence, the evolution processes of these dynamic systems can be abstracted as the evolution processes of information. Furthermore, we present the formulas for information flow, information dissipation rate, and entropy production rate. We prove that the information production probably emerges in a dynamic system with internal attractive interaction between the elements, and derive a formula for this information
Comparing cosmic web classifiers using information theory
NASA Astrophysics Data System (ADS)
Leclercq, Florent; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin
2016-08-01
We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.
Information and communication in polygon theories
NASA Astrophysics Data System (ADS)
Massar, Serge; Patra, Manas K.
2014-05-01
Generalized probabilistic theories (GPTs) provide a framework in which one can formulate physical theories that includes classical and quantum theories, but also many other alternative theories. In order to compare different GPTs, we advocate an approach in which one views a state in a GPT as a resource and quantifies the cost of interconverting between different such resources. We illustrate this approach on polygon theories [New J. Phys. 13, 063024 (2011)., 10.1088/1367-2630/13/6/063024] that interpolate (as the number n of edges of the polygon increases) between a classical trit (when n =3) and a real quantum bit (when n =∞). Our main results are that simulating the transmission of a single n-gon state requires more than one qubit, or more than log(log(n)) bits, and that n-gon states with n odd cannot be simulated by n'-gon states with n' even (for all n ,n'). These results are obtained by showing that the classical capacity of a single n-gon state with n even is 1 bit, whereas it is larger than 1 bit when n is odd; by showing that transmitting a single n-gon state with n even violates information causality; and by studying the communication complexity cost of the nondeterministic not-equal function using n-gon states.
Topological rubber elasticity theory. II. SCL networks
NASA Astrophysics Data System (ADS)
Iwata, Kazuyoshi
1982-06-01
The theory presented in part I [Iwata, J. Chem. Phys. 76, 6363 (1982)] is applied to networks having a simple-cubic-lattice (SCL) regular connection pattern, for which the projection matrix Γ* is computed easily. Derivatives of elastic free energies in regard to parameter λ for macroscopic deformation ∂F˜e/∂λ are computed numerically for isotropic deformations (swelling or deswelling) and for simple deformations (extension or contraction under swelling by α times). The initial arrangement of junction points r0 is assumed to be exactly SCL, and δ = d0/√νb is chosen as one of parameters in the calculation, where d0 is an end-to-end distance of the strands at the time of network formation, ν is a degree of polymerization in regard to the strands, and b is a statistical length per monomer. A repeating cell is chosen as a cube composed of 3×3×3 ( = 27) junction points and 3×27 ( = 81) strands. The following are found in this work. (1) Among four terms ∂F0,ph/∂λ, ∂F˜0,top/∂λ, ∂F˜1/∂λ, and ∂F˜2/∂λ of the derivative of the elastic free energy, the principal term is ∂F˜0,top/∂λ, which comes from the topological interaction among the strands; the phantom network term ∂F0,ph/∂λ is only a small correction to the net stress. (2) In isotropic deformations, the elastic free energy takes a minimum at λ0, a little below λ = 1; for compression below λ0, a strong postitive inner pressure, which comes from the topological repulsive forces among the strands, arises. (3) In simple deformations, the Mooney-Rivlin term appears for unswollen systems and it disappears as swelling of the network proceeds. Experimental plans are proposed which will reveal the existence of the topological repulsive interactions in the networks.
Information and Entropy in Quantum Theory
NASA Astrophysics Data System (ADS)
Maroney, O. J. E.
2004-11-01
We look at certain thought experiments based upon the 'delayed choice' and 'quantum eraser' interference experiments, which present a complementarity between information gathered from a quantum measurement and interference effects. It has been argued that these experiments show the Bohm interpretation of quantum theory is untenable. We demonstrate that these experiments depend critically upon the assumption that a quantum optics device can operate as a measuring device, and show that, in the context of these experiments, it cannot be consistently understood in this way. By contrast, we then show how the notion of 'active information' in the Bohm interpretation provides a coherent explanation of the phenomena shown in these experiments. We then examine the relationship between information and entropy. The thought experiment connecting these two quantities is the Szilard Engine version of Maxwell's Demon, and it has been suggested that quantum measurement plays a key role in this. We provide the first complete description of the operation of the Szilard Engine as a quantum system. This enables us to demonstrate that the role of quantum measurement suggested is incorrect, and further, that the use of information theory to resolve Szilard's paradox is both unnecessary and insufficient. Finally we show that, if the concept of 'active information' is extended to cover thermal density matrices, then many of the conceptual problems raised by this paradox appear to be resolved.
Information processing in convex operational theories
Barnum, Howard Nelch; Wilce, Alexander G
2008-01-01
In order to understand the source and extent of the greater-than-classical information processing power of quantum systems, one wants to characterize both classical and quantum mechanics as points in a broader space of possible theories. One approach to doing this, pioneered by Abramsky and Coecke, is to abstract the essential categorical features of classical and quantum mechanics that support various information-theoretic constraints and possibilities, e.g., the impossibility of cloning in the latter, and the possibility of teleportation in both. Another approach, pursued by the authors and various collaborators, is to begin with a very conservative, and in a sense very concrete, generalization of classical probability theory--which is still sufficient to encompass quantum theory--and to ask which 'quantum' informational phenomena can be reproduced in this much looser setting. In this paper, we review the progress to date in this second programme, and offer some suggestions as to how to link it with the categorical semantics for quantum processes developed by Abramsky and Coecke.
An information-based network approach for protein classification
Wan, Xiaogeng; Zhao, Xin; Yau, Stephen S. T.
2017-01-01
Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. PMID:28350835
Adaptive Local Information Transfer in Random Boolean Networks.
Haruna, Taichi
2017-01-01
Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.
Networks in financial markets based on the mutual information rate.
Fiedor, Paweł
2014-05-01
In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.
Networks in financial markets based on the mutual information rate
NASA Astrophysics Data System (ADS)
Fiedor, Paweł
2014-05-01
In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.
Minimum energy information fusion in sensor networks
Chapline, G
1999-05-11
In this paper we consider how to organize the sharing of information in a distributed network of sensors and data processors so as to provide explanations for sensor readings with minimal expenditure of energy. We point out that the Minimum Description Length principle provides an approach to information fusion that is more naturally suited to energy minimization than traditional Bayesian approaches. In addition we show that for networks consisting of a large number of identical sensors Kohonen self-organization provides an exact solution to the problem of combing the sensor outputs into minimal description length explanations.
Possibilistic systems within a general information theory
Joslyn, C.
1999-06-01
The author surveys possibilistic systems theory and place it in the context of Imprecise Probabilities and General Information Theory (GIT). In particular, he argues that possibilistic systems hold a distinct position within a broadly conceived, synthetic GIT. The focus is on systems and applications which are semantically grounded by empirical measurement methods (statistical counting), rather than epistemic or subjective knowledge elicitation or assessment methods. Regarding fuzzy measures as special provisions, and evidence measures (belief and plausibility measures) as special fuzzy measures, thereby he can measure imprecise probabilities directly and empirically from set-valued frequencies (random set measurement). More specifically, measurements of random intervals yield empirical fuzzy intervals. In the random set (Dempster-Shafer) context, probability and possibility measures stand as special plausibility measures in that their distributionality (decomposability) maps directly to an aggregable structure of the focal classes of their random sets. Further, possibility measures share with imprecise probabilities the ability to better handle open world problems where the universe of discourse is not specified in advance. In addition to empirically grounded measurement methods, possibility theory also provides another crucial component of a full systems theory, namely prediction methods in the form of finite (Markov) processes which are also strictly analogous to the probabilistic forms.
Groups, information theory, and Einstein's likelihood principle
NASA Astrophysics Data System (ADS)
Sicuro, Gabriele; Tempesta, Piergiulio
2016-04-01
We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts.
Groups, information theory, and Einstein's likelihood principle.
Sicuro, Gabriele; Tempesta, Piergiulio
2016-04-01
We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts.
Astrophysical data analysis with information field theory
Enßlin, Torsten
2014-12-05
Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.
Information theory applications for biological sequence analysis.
Vinga, Susana
2014-05-01
Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.
Nonlinear adaptive networks: A little theory, a few applications
Jones, R.D.; Qian, S.; Barnes, C.W.; Bisset, K.R.; Bruce, G.M.; Lee, K.; Lee, L.A.; Mead, W.C.; O'Rourke, M.K.; Thode, L.E. ); Lee, Y.C.; Flake, G.W. Maryland Univ., College Park, MD ); Poli, I.J. Bologna Univ. )
1990-01-01
We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We than present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series tidal prediction in Venice Lagoon, sonar transient detection, control of nonlinear processes, balancing a double inverted pendulum and design advice for free electron lasers. 26 refs., 23 figs.
An anti-attack model based on complex network theory in P2P networks
NASA Astrophysics Data System (ADS)
Peng, Hao; Lu, Songnian; Zhao, Dandan; Zhang, Aixin; Li, Jianhua
2012-04-01
Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on a dynamic compensation process and a reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.
Information Filtering on Coupled Social Networks
Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui
2014-01-01
In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525
Information transfer network of global market indices
NASA Astrophysics Data System (ADS)
Kim, Yup; Kim, Jinho; Yook, Soon-Hyung
2015-07-01
We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.
Information filtering on coupled social networks.
Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui
2014-01-01
In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.
Directedness of information flow in mobile phone communication networks.
Peruani, Fernando; Tabourier, Lionel
2011-01-01
Without having direct access to the information that is being exchanged, traces of information flow can be obtained by looking at temporal sequences of user interactions. These sequences can be represented as causality trees whose statistics result from a complex interplay between the topology of the underlying (social) network and the time correlations among the communications. Here, we study causality trees in mobile-phone data, which can be represented as a dynamical directed network. This representation of the data reveals the existence of super-spreaders and super-receivers. We show that the tree statistics, respectively the information spreading process, are extremely sensitive to the in-out degree correlation exhibited by the users. We also learn that a given information, e.g., a rumor, would require users to retransmit it for more than 30 hours in order to cover a macroscopic fraction of the system. Our analysis indicates that topological node-node correlations of the underlying social network, while allowing the existence of information loops, they also promote information spreading. Temporal correlations, and therefore causality effects, are only visible as local phenomena and during short time scales. Consequently, the very idea that there is (intentional) information spreading beyond a small vecinity is called into question. These results are obtained through a combination of theory and data analysis techniques.
Optimal Network Modularity for Information Diffusion
NASA Astrophysics Data System (ADS)
Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol
2014-08-01
We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.
Vulnerability Assessment Tools for Complex Information Networks
2006-11-14
developed intrusion detection algorithms for two routing protocols: AODV and OLSR . The results showed that our systems can detect most of attacks ...information networks against information-based attack . Accomplishments during the current reporting period are documented in 49 publications and 1 patent...2003. 5. Cassandras, C.G., “Detecting and Reacting to DoS Attacks ”, ARO Grantee Meeting, Cambridge, MA, July 2003. 6. Cassandras, C.G
The Embedded Self: A Social Networks Approach to Identity Theory
ERIC Educational Resources Information Center
Walker, Mark H.; Lynn, Freda B.
2013-01-01
Despite the fact that key sociological theories of self and identity view the self as fundamentally rooted in networks of interpersonal relationships, empirical research investigating how personal network structure influences the self is conspicuously lacking. To address this gap, we examine links between network structure and role identity…
Information theory, novelty and hippocampal responses: unpredicted or unpredictable?
Strange, Bryan A; Duggins, Andrew; Penny, William; Dolan, Raymond J; Friston, Karl J
2005-04-01
Shannon's information theory provides a principled framework for the quantitative analysis of brain responses during the encoding and representation of event streams. In particular, entropy measures the expected uncertainty of events in a given context. This contextual uncertainty or unpredictability may, itself, be important for balancing [bottom-up] sensory information and [top-down] prior expectations during perceptual synthesis. Using event-related functional magnetic resonance imaging (fMRI), we found that the anterior hippocampus is sensitive to the entropy of a visual stimulus stream. In contrast, activity in an extensive bilateral cortico-thalamic network was dictated by the surprise or information associated with each particular stimulus. In short, we show that the probabilistic structure or context in which events occur is an important predictor of hippocampal activity.
Random matrix techniques in quantum information theory
Collins, Benoît; Nechita, Ion
2016-01-15
The purpose of this review is to present some of the latest developments using random techniques, and in particular, random matrix techniques in quantum information theory. Our review is a blend of a rather exhaustive review and of more detailed examples—coming mainly from research projects in which the authors were involved. We focus on two main topics, random quantum states and random quantum channels. We present results related to entropic quantities, entanglement of typical states, entanglement thresholds, the output set of quantum channels, and violations of the minimum output entropy of random channels.
Random matrix techniques in quantum information theory
NASA Astrophysics Data System (ADS)
Collins, Benoît; Nechita, Ion
2016-01-01
The purpose of this review is to present some of the latest developments using random techniques, and in particular, random matrix techniques in quantum information theory. Our review is a blend of a rather exhaustive review and of more detailed examples—coming mainly from research projects in which the authors were involved. We focus on two main topics, random quantum states and random quantum channels. We present results related to entropic quantities, entanglement of typical states, entanglement thresholds, the output set of quantum channels, and violations of the minimum output entropy of random channels.
Does information theory explain biological evolution?
NASA Astrophysics Data System (ADS)
Battail, G.
1997-11-01
It is suggested that Dawkins' model of evolution needs error-correction coding in the genome replication process. Nested coding is moreover assumed. Consequences of these hypotheses are drawn using fundamental results of information theory. Genome replication is dealt with independently of phenotype encoding, which pertains to semantics. The proposed hypotheses enable explaining facts of genetics and evolution, including the existence of redundant DNA (the introns), the observed correlation between the rate of mutations on the one hand, the genome length and the redundancy rate on the other hand, the discreteness of species and the trend of eukaryotes evolution towards complexity.
Network anomaly detection system with optimized DS evidence theory.
Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu
2014-01-01
Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.
A Complexity Theory of Neural Networks
1990-04-14
Significant results have been obtained on the computation complexity of analog neural networks , and distribute voting. The computing power and...learning algorithms for limited precision analog neural networks have been investigated. Lower bounds for constant depth, polynomial size analog neural ... networks , and a limited version of discrete neural networks have been obtained. The work on distributed voting has important applications for distributed
OASIS: Prototyping Graphical Interfaces to Networked Information.
ERIC Educational Resources Information Center
Buckland, Michael K.; And Others
1993-01-01
Describes the latest modifications being made to OASIS, a front-end enhancement to the University of California's MELVYL online union catalog. Highlights include the X Windows interface; multiple database searching to act as an information network; Lisp implementation for flexible data representation; and OASIS commands and features to help…
Networked Information Resources. SPEC Kit 253.
ERIC Educational Resources Information Center
Bleiler, Richard, Comp.; Plum, Terry, Comp.
1999-01-01
This SPEC Kit, published six times per year, examines how Association of Research Libraries (ARL) libraries have structured themselves to identify networked information resources in the market, to evaluate them for purchase, to make purchasing decisions, to publicize them, and to assess their continued utility. In the summer of 1999, the survey…
Networked Information Resources. SPEC Kit 253.
ERIC Educational Resources Information Center
Bleiler, Richard, Comp.; Plum, Terry, Comp.
1999-01-01
This SPEC Kit, published six times per year, examines how Association of Research Libraries (ARL) libraries have structured themselves to identify networked information resources in the market, to evaluate them for purchase, to make purchasing decisions, to publicize them, and to assess their continued utility. In the summer of 1999, the survey…
OASIS: Prototyping Graphical Interfaces to Networked Information.
ERIC Educational Resources Information Center
Buckland, Michael K.; And Others
1993-01-01
Describes the latest modifications being made to OASIS, a front-end enhancement to the University of California's MELVYL online union catalog. Highlights include the X Windows interface; multiple database searching to act as an information network; Lisp implementation for flexible data representation; and OASIS commands and features to help…
Networked Information: Finding What's Out There.
ERIC Educational Resources Information Center
Lynch, Clifford A.
1997-01-01
Clifford A. Lynch, developer of MELVYL and former director of library automation at the University of California, is now executive director for the Coalition for Networked Information (CNI). This interview discusses Lynch's background, MELVYL, the Web and the role of libraries and librarians, community and collaborative filtering, the library of…
Theory And Uses Of A Broad Band Coaxial Cable Information System In The Hospital Environment
NASA Astrophysics Data System (ADS)
Edwin, Allan I.; Harder, Edward L.
1982-01-01
This paper explains the theory and operation of local area networks which employ broadband coaxial cable communication technology. Such networks combine data, voice and T.V. communications on a single one-half inch diameter coaxial cable. The 300 Megahertz (MHZ) bandwidth of the network also provides multiple high speed (multi-million bit per second) data channels required for medical image processing applications. The theory is then applied to the development of the total facility (hospital complex) Information Network providing services which include security, facilities management, data processing, and emergency paging as well as medical image data transmissions.
A brief review of molecular information theory
Schneider, Thomas D.
2011-01-01
The idea that we could build molecular communications systems can be advanced by investigating how actual molecules from living organisms function. Information theory provides tools for such an investigation. This review describes how we can compute the average information in the DNA binding sites of any genetic control protein and how this can be extended to analyze its individual sites. A formula equivalent to Claude Shannon’s channel capacity can be applied to molecular systems and used to compute the efficiency of protein binding. This efficiency is often 70% and a brief explanation for that is given. The results imply that biological systems have evolved to function at channel capacity, which means that we should be able to build molecular communications that are just as robust as our macroscopic ones. PMID:22110566
An information theory of image gathering
NASA Technical Reports Server (NTRS)
Fales, Carl L.; Huck, Friedrich O.
1991-01-01
Shannon's mathematical theory of communication is extended to image gathering. Expressions are obtained for the total information that is received with a single image-gathering channel and with parallel channels. It is concluded that the aliased signal components carry information even though these components interfere with the within-passband components in conventional image gathering and restoration, thereby degrading the fidelity and visual quality of the restored image. An examination of the expression for minimum mean-square-error, or Wiener-matrix, restoration from parallel image-gathering channels reveals a method for unscrambling the within-passband and aliased signal components to restore spatial frequencies beyond the sampling passband out to the spatial frequency response cutoff of the optical aperture.
BOOK REVIEW: Theory of Neural Information Processing Systems
NASA Astrophysics Data System (ADS)
Galla, Tobias
2006-04-01
It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the
The need-informational theory of emotions.
Simonov, P V
1984-03-01
As an evolvement of Pavlov ideas on higher nervous (psychic) activity 'the need-informational theory of emotions' was suggested by the author in 1964. According to it an emotion is a function of two major factors: (1) power and quality of actual need (or drive, or motivation) and (2) estimation of probability (possibility) of need satisfaction on the basis of phylo- and ontogenetic experience. In the process of experimental testing of 'the need-informational theory of emotions' the role of different cerebral structures (frontal neocortex, hippocampus, amygdala, hypothalamus) in the genesis of emotional states and in the organization of goal-directed behavior was elucidated. The experimental data showed that these 4 brain structures play the major role in estimation of signals coming from environment and in the choice of subject's reactions. The individual characteristics of the interaction between the 4 brain structures must be taken into consideration in discussing neurophysiological backgrounds of different types of the higher nervous activity (temperaments), parameters of extra-introversion and neurotism (emotionality), the formation of main types of neurosis.
MIDER: Network Inference with Mutual Information Distance and Entropy Reduction
Villaverde, Alejandro F.; Ross, John; Morán, Federico; Banga, Julio R.
2014-01-01
The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information–theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide
Information diffusion in structured online social networks
NASA Astrophysics Data System (ADS)
Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui
2015-05-01
Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.
Community discovery and information flow in networks
NASA Astrophysics Data System (ADS)
Huberman, Bernardo A.
2005-03-01
The dynamics of information within social groups is relevant to issues of productivity, innovation and the sorting out of useful ideas from the general chatter of a community. How information spreads and is aggregated determines the speed with which individuals and organizations can act and plan their future activities. This talk will describe new mechanisms for automatically identifying communities of practice within organizations and for elucidating the spread of information within those communities. Many of these mechanisms rely on the scale-free nature of social networks.
Improving information filtering via network manipulation
NASA Astrophysics Data System (ADS)
Zhang, Fuguo; Zeng, An
2012-12-01
The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.
Complementarity and entanglement in quantum information theory
NASA Astrophysics Data System (ADS)
Tessier, Tracey Edward
This research investigates two inherently quantum mechanical phenomena, namely complementarity and entanglement, from an information-theoretic perspective. Beyond philosophical implications, a thorough grasp of these concepts is crucial for advancing our understanding of foundational issues in quantum mechanics, as well as in studying how the use of quantum systems might enhance the performance of certain information processing tasks. The primary goal of this thesis is to shed light on the natures and interrelationships of these phenomena by approaching them from the point of view afforded by information theory. We attempt to better understand these pillars of quantum mechanics by studying the various ways in which they govern the manipulation of information, while at the same time gaining valuable insight into the roles they play in specific applications. The restrictions that nature places on the distribution of correlations in a multipartite quantum system play fundamental roles in the evolution of such systems and yield vital insights into the design of protocols for the quantum control of ensembles with potential applications in the field of quantum computing. By augmenting the existing formalism for quantifying entangled correlations, we show how this entanglement sharing behavior may be studied in increasingly complex systems of both theoretical and experimental significance. Further, our results shed light on the dynamical generation and evolution of multipartite entanglement by demonstrating that individual members of an ensemble of identical systems coupled to a common probe can become entangled with one another, even when they do not interact directly. The findings presented in this thesis support the conjecture that Hilbert space dimension is an objective property of a quantum system since it constrains the number of valid conceptual divisions of the system into subsystems. These arbitrary observer-induced distinctions are integral to the theory since
Insights into the organization of biochemical regulatory networks using graph theory analyses.
Ma'ayan, Avi
2009-02-27
Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.
New Learning Communities: Collaboration, Networking, and Information Literacy.
ERIC Educational Resources Information Center
Tompkins, Philip; Perry, Susan; Lippincott, Joan K.
1998-01-01
Discusses the New Learning Communities (NLC) program developed by CNI (Coalition for Networked Information) to support pioneers in education who use networking and networked information to support student-centered teaching and learning. Highlights include computer networks in higher education, especially the Internet; information literacy skills;…
Application of random matrix theory to biological networks
NASA Astrophysics Data System (ADS)
Luo, Feng; Zhong, Jianxin; Yang, Yunfeng; Scheuermann, Richard H.; Zhou, Jizhong
2006-09-01
We show that spectral fluctuation of interaction matrices of a yeast protein protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.
Network Anomaly Detection System with Optimized DS Evidence Theory
Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu
2014-01-01
Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258
A Preliminary Theory of Dark Network Resilience
ERIC Educational Resources Information Center
Bakker, Rene M.; Raab, Jorg; Milward, H. Brinton
2012-01-01
A crucial contemporary policy question for governments across the globe is how to cope with international crime and terrorist networks. Many such "dark" networks--that is, networks that operate covertly and illegally--display a remarkable level of resilience when faced with shocks and attacks. Based on an in-depth study of three cases…
Reinforce Networking Theory with OPNET Simulation
ERIC Educational Resources Information Center
Guo, Jinhua; Xiang, Weidong; Wang, Shengquan
2007-01-01
As networking systems have become more complex and expensive, hands-on experiments based on networking simulation have become essential for teaching the key computer networking topics to students. The simulation approach is the most cost effective and highly useful because it provides a virtual environment for an assortment of desirable features…
Reinforce Networking Theory with OPNET Simulation
ERIC Educational Resources Information Center
Guo, Jinhua; Xiang, Weidong; Wang, Shengquan
2007-01-01
As networking systems have become more complex and expensive, hands-on experiments based on networking simulation have become essential for teaching the key computer networking topics to students. The simulation approach is the most cost effective and highly useful because it provides a virtual environment for an assortment of desirable features…
A Preliminary Theory of Dark Network Resilience
ERIC Educational Resources Information Center
Bakker, Rene M.; Raab, Jorg; Milward, H. Brinton
2012-01-01
A crucial contemporary policy question for governments across the globe is how to cope with international crime and terrorist networks. Many such "dark" networks--that is, networks that operate covertly and illegally--display a remarkable level of resilience when faced with shocks and attacks. Based on an in-depth study of three cases…
Predictability: Recent insights from information theory
NASA Astrophysics Data System (ADS)
Delsole, Timothy; Tippett, Michael K.
2007-12-01
This paper summarizes a framework for investigating predictability based on information theory. This framework connects and unifies a wide variety of statistical methods traditionally used in predictability analysis, including linear regression, canonical correlation analysis, singular value decomposition, discriminant analysis, and data assimilation. Central to this framework is a procedure called predictable component analysis (PrCA). PrCA optimally decomposes variables by predictability, just as principal component analysis optimally decomposes variables by variance. For normal distributions the same predictable components are obtained whether one optimizes predictive information, the dispersion part of relative entropy, mutual information, Mahalanobis error, average signal to noise ratio, normalized mean square error, or anomaly correlation. For joint normal distributions, PrCA is equivalent to canonical correlation analysis between forecast and observations. The regression operator that maps observations to forecasts plays an important role in this framework, with the left singular vectors of this operator being the predictable components and the singular values being the canonical correlations. This correspondence between predictable components and singular vectors occurs only if the singular vectors are computed using Mahalanobis norms, a result that sheds light on the role of norms in predictability. In linear stochastic models the forcing that minimizes predictability is the one that renders the "whitened" dynamical operator normal. This condition for minimum predictability is invariant to linear transformation and is equivalent to detailed balance. The framework also inspires some new approaches to accounting for deficiencies of forecast models and estimating distributions from finite samples.
Percolation theory on interdependent networks based on epidemic spreading
NASA Astrophysics Data System (ADS)
Son, Seung-Woo; Bizhani, Golnoosh; Christensen, Claire; Grassberger, Peter; Paczuski, Maya
2012-01-01
We consider percolation on interdependent locally treelike networks, recently introduced by Buldyrev S. V. et al., Nature, 464 (2010) 1025, and demonstrate that the problem can be simplified conceptually by deleting all references to cascades of failures. Such cascades do exist, but their explicit treatment just complicates the theory —which is a straightforward extension of the usual epidemic spreading theory on a single network. Our method has the added benefits that it is directly formulated in terms of an order parameter and its modular structure can be easily extended to other problems, e.g. to any number of interdependent networks, or to networks with dependency links.
Informal Theory: The Ignored Link in Theory-to-Practice
ERIC Educational Resources Information Center
Love, Patrick
2012-01-01
Applying theory to practice in student affairs is dominated by the assumption that formal theory is directly applied to practice. Among the problems with this assumption is that many practitioners believe they must choose between their lived experiences and formal theory, and that graduate students are taught that their experience "does not…
Informal Theory: The Ignored Link in Theory-to-Practice
ERIC Educational Resources Information Center
Love, Patrick
2012-01-01
Applying theory to practice in student affairs is dominated by the assumption that formal theory is directly applied to practice. Among the problems with this assumption is that many practitioners believe they must choose between their lived experiences and formal theory, and that graduate students are taught that their experience "does not…
Mean-field theory of echo state networks
NASA Astrophysics Data System (ADS)
Massar, Marc; Massar, Serge
2013-04-01
Dynamical systems driven by strong external signals are ubiquitous in nature and engineering. Here we study “echo state networks,” networks of a large number of randomly connected nodes, which represent a simple model of a neural network, and have important applications in machine learning. We develop a mean-field theory of echo state networks. The dynamics of the network is captured by the evolution law, similar to a logistic map, for a single collective variable. When the network is driven by many independent external signals, this collective variable reaches a steady state. But when the network is driven by a single external signal, the collective variable is non stationary but can be characterized by its time averaged distribution. The predictions of the mean-field theory, including the value of the largest Lyapunov exponent, are compared with the numerical integration of the equations of motion.
The theory of pattern formation on directed networks.
Asllani, Malbor; Challenger, Joseph D; Pavone, Francesco Saverio; Sacconi, Leonardo; Fanelli, Duccio
2014-07-31
Dynamical processes on networks have generated widespread interest in recent years. The theory of pattern formation in reaction-diffusion systems defined on symmetric networks has often been investigated, due to its applications in a wide range of disciplines. Here we extend the theory to the case of directed networks, which are found in a number of different fields, such as neuroscience, computer networks and traffic systems. Owing to the structure of the network Laplacian, the dispersion relation has both real and imaginary parts, at variance with the case for a symmetric, undirected network. The homogeneous fixed point can become unstable due to the topology of the network, resulting in a new class of instabilities, which cannot be induced on undirected graphs. Results from a linear stability analysis allow the instability region to be analytically traced. Numerical simulations show travelling waves, or quasi-stationary patterns, depending on the characteristics of the underlying graph.
Theoretical approaches for the dynamics of complex biological systems from information of networks
MOCHIZUKI, Atsushi
2016-01-01
Modern biology has provided many examples of large networks describing the interactions between multiple species of bio-molecules. It is believed that the dynamics of molecular activities based on such networks are the origin of biological functions. On the other hand, we have a limited understanding for dynamics of molecular activity based on networks. To overcome this problem, we have developed two structural theories, by which the important aspects of the dynamical properties of the system are determined only from information on the network structure, without assuming other quantitative details. The first theory, named Linkage Logic, determines a subset of molecules in regulatory networks, by which any long-term dynamical behavior of the whole system can be identified/controlled. The second theory, named Structural Sensitivity Analysis, determines the sensitivity responses of the steady state of chemical reaction networks to perturbations of the reaction rate. The first and second theories investigate the dynamical properties of regulatory and reaction networks, respectively. The first theory targets the attractors of the regulatory network systems, whereas the second theory applies only to the steady states of the reaction network systems, but predicts their detailed behavior. To demonstrate the utility of our methods several biological network systems, and show they are practically useful to analyze behaviors of biological systems. PMID:27725468
Characterization of vehicle behavior with information theory
NASA Astrophysics Data System (ADS)
Aquino, Andre L. L.; Cavalcante, Tamer S. G.; Almeida, Eliana S.; Frery, Alejandro C.; Rosso, Osvaldo A.
2015-10-01
This work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: (i) Mobile Century data set collected on Highway I-880, near Union City, California; (ii) Borlänge GPS data set collected in the Swedish city of Borlänge; and (iii) Beijing taxicabs data set collected in Beijing, China, where each vehicle speed is stored as a time series. The Bandt-Pompe methodology combined with the Complexity-Entropy plane were used to identify different regimes and behaviors. The global velocity is compatible with a correlated noise with f - k Power Spectrum with k ≥ 0. With this we identify traffic behaviors as, for instance, random velocities ( k ≃ 0) when there is congestion, and more correlated velocities ( k ≃ 3) in the presence of free traffic flow.
The network perspective: an integration of attachment and family systems theories.
Kozlowska, Kasia; Hanney, Lesley
2002-01-01
In this article we discuss the network paradigm as a useful base from which to integrate attachment and family systems theories. The network perspective refers to the application of general systems theory to living systems, and provides a framework that conceptualizes the dyadic and family systems as simultaneously distinct and interconnected. Network thinking requires that the clinician holds multiple perspectives in mind, considers each system level as both a part and a whole, and shifts the focus of attention between levels as required. Key epistemological issues that have hindered the integration of the theories are discussed. These include inconsistencies within attachment theory itself and confusion surrounding the theoretical conceptualizations of the relationship between attachment and family systems theories. Detailed information about attachment categories is provided using the Dynamic Maturational model. Case vignettes illustrating work with young children and their families explore the clinical implications of integrating attachment data into family therapy practice.
Distribution of Information in Ad Hoc Networks
2007-09-01
requiring coverage. It means that when the density of the network is important precision approaches 1 (perfect case). 0l 1l 2l 3l 4l .3 100 ...information decreases, and vice versa. For the case where the number of nodes is 100 in the 800m*800m zone, the density is light; the distribution of those...information source nodes in a graph of infinite density . Thus, nodes can be laid out where desired. Second, it provides an algorithm which achieves an
Complex Networks/Foundations of Information Systems
2013-03-06
Information Systems 6 March 2013 Robert J. Bonneau, Ph.D. Division Chief AFOSR/RTC Report Documentation Page Form ApprovedOMB No. 0704-0188 Public...if it does not display a currently valid OMB control number. 1. REPORT DATE 06 MAR 2013 2. REPORT TYPE 3. DATES COVERED 00-00-2013 to 00-00...2013 4. TITLE AND SUBTITLE Complex Networks/Foundations of Information Systems 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6
Using information networks for competitive advantage.
Rothenberg, R L
1995-01-01
Although the healthcare "information superhighway" has received considerable attention, the use of information technology to create a sustainable competitive advantage is not new to other industries. Economic survival in the new world of managed care may depend on a healthcare delivery system's ability to use network-based communications technologies to differentiate itself in the market, especially through cost savings and demonstration of desirable outcomes. The adaptability of these technologies can help position healthcare organizations to break the paradigms of the past and thrive in a market environment that stresses coordination, efficiency, and quality in various settings.
Integrating Information Networks for Collective Planetary Stewardship
NASA Astrophysics Data System (ADS)
Tiwari, A.
2016-12-01
Responsible behaviour resulting from climate literacy in global environmental movement is limited to policy and planning institutions in the Global South, while remaining absent for ends-user. Thus, planetary stewardship exists only at earth system boundaries where pressures sink to the local scale while ethics remains afloat. Existing citizen participation is restricted within policy spheres, appearing synonymous to enforcements in social psychology. Much, accounted reason is that existing information mechanisms operate mostly through linear exchanges between institutions and users, therefore reinforcing only hierarchical relationships. This study discloses such relationships that contribute to broad networking gaps through information demand assessment of stakeholders in a dozen development projects based in South Asia. Two parameters widely used for this purpose are: a. Feedback: Ends-user feedback to improve consumption literacy of climate sensitive resources (through consumption displays, billing, advisory services ecolabelling, sensors) and, b. Institutional Policy: Rewarding punishing to enforce desired behaviour (subsidies, taxation). Research answered: 1. Who gets the information (Equity in Information Distribution)? As existing information publishing mechanisms are designed by and for analysts, 2. How information translates to climate action Transparency of Execution)? Findings suggested that climate goals manifested in economic policy, than environmental policy, have potential clear short-term benefits and costs, and coincide with people's economic goals Also grassroots roles for responsible behaviour are empowered with presence of end user information. Barier free climate communication process and decision making is ensured among multiplicity of stakeholders with often conflicting perspectives. Research finds significance where collaboration among information networks can better translate regional policies into local action for climate adaptation and
Identification of Boolean Networks Using Premined Network Topology Information.
Zhang, Xiaohua; Han, Huaxiang; Zhang, Weidong
2017-02-01
This brief aims to reduce the data requirement for the identification of Boolean networks (BNs) by using the premined network topology information. First, a matching table is created and used for sifting the true from the false dependences among the nodes in the BNs. Then, a dynamic extension to matching table is developed to enable the dynamic locating of matching pairs to start as soon as possible. Next, based on the pseudocommutative property of the semitensor product, a position-transform mining is carried out to further improve data utilization. Combining the above, the topology of the BNs can be premined for the subsequent identification. Examples are given to illustrate the efficiency of reducing the data requirement. Some excellent features, such as the online and parallel processing ability, are also demonstrated.
Network Data: Statistical Theory and New Models
2016-02-17
research covered a wide range of topics in statistics including analysis and methods for spectral clustering for sparse and structured networks...signals, bootstrapping, Lasso+OLS, confidence interval, concise comparative summarization, EM algorithm, spectral clustering , aerosol retrieval...covered a wide range of topics in statistics including analysis and methods for spectral clustering for sparse and structured networks [2,7,8,21
Optimal Learning Paths in Information Networks
Rodi, G. C.; Loreto, V.; Servedio, V. D. P.; Tria, F.
2015-01-01
Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances. PMID:26030508
Data update in a land information network
NASA Astrophysics Data System (ADS)
Mullin, Robin C.
1988-01-01
The on-going update of data exchanged in a land information network is examined. In the past, major developments have been undertaken to enable the exchange of data between land information systems. A model of a land information network and the data update process have been developed. Based on these, a functional description of the database and software to perform data updating is presented. A prototype of the data update process was implemented using the ARC/INFO geographic information system. This was used to test four approaches to data updating, i.e., bulk, block, incremental, and alert updates. A bulk update is performed by replacing a complete file with an updated file. A block update requires that the data set be partitioned into blocks. When an update occurs, only the blocks which are affected need to be transferred. An incremental update approach records each feature which is added or deleted and transmits only the features needed to update the copy of the file. An alert is a marker indicating that an update has occurred. It can be placed in a file to warn a user that if he is active in an area containing markers, updated data is available. The four approaches have been tested using a cadastral data set.
Optimal learning paths in information networks.
Rodi, G C; Loreto, V; Servedio, V D P; Tria, F
2015-06-01
Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.
Pairwise network information and nonlinear correlations
NASA Astrophysics Data System (ADS)
Martin, Elliot A.; Hlinka, Jaroslav; Davidsen, Jörn
2016-10-01
Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle, this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases, this is not an option since the bivariate distributions needed may not be reliably estimated or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate probability distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples.
Pairwise network information and nonlinear correlations.
Martin, Elliot A; Hlinka, Jaroslav; Davidsen, Jörn
2016-10-01
Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle, this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases, this is not an option since the bivariate distributions needed may not be reliably estimated or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate probability distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples.
An Attractor Network in the Hippocampus: Theory and Neurophysiology
ERIC Educational Resources Information Center
Rolls, Edmund T.
2007-01-01
A quantitative computational theory of the operation of the CA3 system as an attractor or autoassociation network is described. Based on the proposal that CA3-CA3 autoassociative networks are important for episodic or event memory in which space is a component (place in rodents and spatial view in primates), it has been shown behaviorally that the…
An Attractor Network in the Hippocampus: Theory and Neurophysiology
ERIC Educational Resources Information Center
Rolls, Edmund T.
2007-01-01
A quantitative computational theory of the operation of the CA3 system as an attractor or autoassociation network is described. Based on the proposal that CA3-CA3 autoassociative networks are important for episodic or event memory in which space is a component (place in rodents and spatial view in primates), it has been shown behaviorally that the…
A Systems Theory View of Organizations as Communication Networks.
ERIC Educational Resources Information Center
Schwartz, Donald F.
Focusing on the analysis of communication networks within organizations with an eye toward implications for study of external communication, this paper (1) develops a systems theory/communication view of the nature of formal organizations, (2) illustrates the notion of holistic organizational communication networks in organizations which include…
NASA Astrophysics Data System (ADS)
Vargas, David L.
Emerging quantum simulator technologies provide a new challenge to quantum many body theory. Quantifying the emergent order in and predicting the dynamics of such complex quantum systems requires a new approach. We develop such an approach based on complex network analysis of quantum mutual information. First, we establish the usefulness of quantum mutual information complex networks by reproducing the phase diagrams of transverse Ising and Bose-Hubbard models. By quantifying the complexity of quantum cellular automata we then demonstrate the applicability of complex network theory to non-equilibrium quantum dynamics. We conclude with a study of student collaboration networks, correlating a student's role in a collaboration network with their grades. This work thus initiates a quantitative theory of quantum complexity and provides a new tool for physics education research. (Abstract shortened by ProQuest.).
Instructional Technology: The Information Superhighway, the Internet, Interactive Video Networks.
ERIC Educational Resources Information Center
Odell, Kerry S.; And Others
1994-01-01
Includes "It Boggles the Mind" (Odell); "Merging Your Classroom onto the Information Superhighway" (Murphy); "The World's Largest Computer Network" (Fleck); "The Information Highway in Iowa" (Miller); "Interactive Video Networks in Secondary Schools" (Swan et al.); and "Upgrade to Humancentric…
Curriculum Theory Network [CTN 4: Winter 1969-70].
ERIC Educational Resources Information Center
Herbert, John, Ed.
This issue of the journal of the "Curriculum Theory Network" contains four major articles on aspects of the curriculum. Karplus, using Science Curriculum Improvement Study as an example, presents three guidelines for developing elementary school science curricula: separate "experience" and "concept" goals; use developmental and learning theories;…
Social Capital Theory: Implications for Women's Networking and Learning
ERIC Educational Resources Information Center
Alfred, Mary V.
2009-01-01
This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.
Social Capital Theory: Implications for Women's Networking and Learning
ERIC Educational Resources Information Center
Alfred, Mary V.
2009-01-01
This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.
Applied Hypergame Theory for Network Defense
2013-06-01
research of game theory concepts. The most influential breakthrough would happen at the hands of a troubled genius , John F. Nash, Jr. 1.2.2 Nash...49] Vane, Russell R. “Hypergame Theory for DTGT Agents”. American Association for Artificial Intelligence, 2000. [50] Vane, Russell R., III. “Planning
A theory that predicts behaviors of disordered cytoskeletal networks.
Belmonte, Julio M; Leptin, Maria; Nédélec, François
2017-09-27
Morphogenesis in animal tissues is largely driven by actomyosin networks, through tensions generated by an active contractile process. Although the network components and their properties are known, and networks can be reconstituted in vitro, the requirements for contractility are still poorly understood. Here, we describe a theory that predicts whether an isotropic network will contract, expand, or conserve its dimensions. This analytical theory correctly predicts the behavior of simulated networks, consisting of filaments with varying combinations of connectors, and reveals conditions under which networks of rigid filaments are either contractile or expansile. Our results suggest that pulsatility is an intrinsic behavior of contractile networks if the filaments are not stable but turn over. The theory offers a unifying framework to think about mechanisms of contractions or expansion. It provides the foundation for studying a broad range of processes involving cytoskeletal networks and a basis for designing synthetic networks. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.
Domain theoretic structures in quantum information theory
NASA Astrophysics Data System (ADS)
Feng, Johnny
2011-12-01
In this thesis, we continue the study of domain theoretic structures in quantum information theory initiated by Keye Martin and Bob Coecke in 2002. The first part of the thesis is focused on exploring the domain theoretic properties of qubit channels. We discover that the Scott continuous qubit channels are exactly those that are unital or constant. We then prove that the unital qubit channels form a continuous dcpo, and identify various measurements on them. We show that Holevo capacity is a measurement on unital qubit channels, and discover the natural measurement in this setting. We find that qubit channels also form a continuous dcpo, but capacity fails to be a measurement. In the second part we focus on the study of exact dcpos, a domain theoretic structure, closely related to continuous dcpos, possessed by quantum states. Exact dcpos admit a topology, called the exact topology, and we show that the exact topology has an order theoretic characterization similar to the characterization of the Scott topology on continuous dcpos. We then explore the connection between exact and continuous dcpos; first, by identifying an important set of points, called the split points, that distinguishes between exact and continuous structures; second, by exploring a continuous completion of exact dcpos, and showing that we can recover the exact topology from the Scott topology of the completion.
Deep Space Network information system architecture study
NASA Technical Reports Server (NTRS)
Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.
1992-01-01
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.
Deep Space Network information system architecture study
NASA Technical Reports Server (NTRS)
Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.
1992-01-01
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.
Network Theory Analysis of Antibody-Antigen Reactivity Data: The Immune Trees at Birth and Adulthood
Bransburg-Zabary, Sharron; Merbl, Yifat; Quintana, Francisco J.; Tauber, Alfred I.; Cohen, Irun R.; Ben-Jacob, Eshel
2011-01-01
Motivation New antigen microarray technology enables parallel recording of antibody reactivities with hundreds of antigens. Such data affords system level analysis of the immune system's organization using methods and approaches from network theory. Here we measured the reactivity of 290 antigens (for both the IgG and IgM isotypes) of 10 healthy mothers and their term newborns. We constructed antigen correlation networks (or immune networks) whose nodes are the antigens and the edges are the antigen-antigen reactivity correlations, and we also computed their corresponding minimum spanning trees (MST) – maximal information reduced sub-graphs. We quantify the network organization (topology) in terms of the network theory divergence rate measure and rank the antigen importance in the full antigen correlation networks by the eigen-value centrality measure. This analysis makes possible the characterization and comparison of the IgG and IgM immune networks at birth (newborns) and adulthood (mothers) in terms of topology and node importance. Results Comparison of the immune network topology at birth and adulthood revealed partial conservation of the IgG immune network topology, and significant reorganization of the IgM immune networks. Inspection of the antigen importance revealed some dominant (in terms of high centrality) antigens in the IgG and IgM networks at birth, which retain their importance at adulthood. PMID:21408156
Information Integration Theory Applied to Attitudes About U. S. Presidents
ERIC Educational Resources Information Center
Anderson, Norman H.
1973-01-01
Information integration theory is a unified general theory that covers several areas of psychology from psychophysics to clinical judgment. It rests on a mathematical basis that has had considerable success in some precise and demanding tests. (Author)
Utilizing general information theories for uncertainty quantification
Booker, J. M.
2002-01-01
Uncertainties enter into a complex problem from many sources: variability, errors, and lack of knowledge. A fundamental question arises in how to characterize the various kinds of uncertainty and then combine within a problem such as the verification and validation of a structural dynamics computer model, reliability of a dynamic system, or a complex decision problem. Because uncertainties are of different types (e.g., random noise, numerical error, vagueness of classification), it is difficult to quantify all of them within the constructs of a single mathematical theory, such as probability theory. Because different kinds of uncertainty occur within a complex modeling problem, linkages between these mathematical theories are necessary. A brief overview of some of these theories and their constituents under the label of Generalized lnforrnation Theory (GIT) is presented, and a brief decision example illustrates the importance of linking at least two such theories.
Critical Theory and Information Studies: A Marcusean Infusion
ERIC Educational Resources Information Center
Pyati, Ajit K.
2006-01-01
In the field of library and information science, also known as information studies, critical theory is often not included in debates about the discipline's theoretical foundations. This paper argues that the critical theory of Herbert Marcuse, in particular, has a significant contribution to make to the field of information studies. Marcuse's…
Critical Theory and Information Studies: A Marcusean Infusion
ERIC Educational Resources Information Center
Pyati, Ajit K.
2006-01-01
In the field of library and information science, also known as information studies, critical theory is often not included in debates about the discipline's theoretical foundations. This paper argues that the critical theory of Herbert Marcuse, in particular, has a significant contribution to make to the field of information studies. Marcuse's…
The problem of applying information theory to efficient image transmission.
NASA Technical Reports Server (NTRS)
Sakrison, D. J.
1973-01-01
The main ideas of Shannon's (1948, 1960) theory of source encoding with a fidelity constraint, more commonly known as rate distortion theory, are summarized. The theory was specifically intended to provide a theoretical basis for efficient transmission of information such as images. What the theory has to contribute to the problem is demonstrated. Difficulties that impeded application of the theory to image transmission, and current efforts to solve these difficulties are discussed.
Genetic networks: between theory and experimentation
NASA Astrophysics Data System (ADS)
Bottani, Samuel; Mazurie, Aurélien
Thanks to an increasing availability of data on cell components and progress in computers and computer science, a long awaited paradigm shift is running in biology from reductionism to holistic approaches. One of the consequences is the huge development of network-related representations of cell activity and an increasing involvement of researchers from computer science, physics and mathematics in their analysis. But what are the promises of these approaches for the biologist? What is the available biological data sustaining them and is it sufficient? After a presentation of the interaction network view of the cell, we shall focus on studies on gene network structure and dynamics. Then we shall discuss the difficulties of these approaches and their theoretical and practical usefulness for the biologist.
Trends in information theory-based chemical structure codification.
Barigye, Stephen J; Marrero-Ponce, Yovani; Pérez-Giménez, Facundo; Bonchev, Danail
2014-08-01
This report offers a chronological review of the most relevant applications of information theory in the codification of chemical structure information, through the so-called information indices. Basically, these are derived from the analysis of the statistical patterns of molecular structure representations, which include primitive global chemical formulae, chemical graphs, or matrix representations. Finally, new approaches that attempt to go "back to the roots" of information theory, in order to integrate other information-theoretic measures in chemical structure coding are discussed.
Lewis Information Network (LINK): Background and overview
NASA Technical Reports Server (NTRS)
Schulte, Roger R.
1987-01-01
The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.
Essential elements of online information networks on invasive alien species
Simpson, A.; Sellers, E.; Grosse, A.; Xie, Y.
2006-01-01
In order to be effective, information must be placed in the proper context and organized in a manner that is logical and (preferably) standardized. Recently, invasive alien species (IAS) scientists have begun to create online networks to share their information concerning IAS prevention and control. At a special networking session at the Beijing International Symposium on Biological Invasions, an online Eastern Asia-North American IAS Information Network (EA-NA Network) was proposed. To prepare for the development of this network, and to provide models for other regional collaborations, we compare four examples of global, regional, and national online IAS information networks: the Global Invasive Species Information Network, the Invasives Information Network of the Inter-American Biodiversity Information Network, the Chinese Species Information System, and the Invasive Species Information Node of the US National Biological Information Infrastructure. We conclude that IAS networks require a common goal, dedicated leaders, effective communication, and broad endorsement, in order to obtain sustainable, long-term funding and long-term stability. They need to start small, use the experience of other networks, partner with others, and showcase benefits. Global integration and synergy among invasive species networks will succeed with contributions from both the top-down and the bottom-up. ?? 2006 Springer.
On the genesis of the idiotypic network theory.
Civello, Andrea
2013-01-01
The idiotypic network theory (INT) was conceived by the Danish immunologist Niels Kaj Jerne in 1973/1974. It proposes an overall view of the immune system as a network of lymphocytes and antibodies. The paper tries to offer a reconstruction of the genesis of the theory, now generally discarded and of mostly historical interest, first of all, by taking into account the context in which Jerne's theoretical proposal was advanced. It is argued the theory challenged, in a sense, the supremacy of the clonal selection theory (CST), this being regarded as the predominant paradigm in the immunological scenario. As CST found shortcomings in explaining certain phenomena, anomalies, one could view INT as a competing paradigm claiming to be able to make sense of such phenomena in its own conceptual framework. After a summary outline of the historical background and some relevant terminological elucidations, a narrative of the various phases of elaboration of the theory is proposed, up to its official public presentation.
Information processing in generalized probabilistic theories
Barrett, Jonathan
2007-03-15
I introduce a framework in which a variety of probabilistic theories can be defined, including classical and quantum theories, and many others. From two simple assumptions, a tensor product rule for combining separate systems can be derived. Certain features, usually thought of as specifically quantum, turn out to be generic in this framework, meaning that they are present in all except classical theories. These include the nonunique decomposition of a mixed state into pure states, a theorem involving disturbance of a system on measurement (suggesting that the possibility of secure key distribution is generic), and a no-cloning theorem. Two particular theories are then investigated in detail, for the sake of comparison with the classical and quantum cases. One of these includes states that can give rise to arbitrary nonsignaling correlations, including the superquantum correlations that have become known in the literature as nonlocal machines or Popescu-Rohrlich boxes. By investigating these correlations in the context of a theory with well-defined dynamics, I hope to make further progress with a question raised by Popescu and Rohrlich, which is why does quantum theory not allow these strongly nonlocal correlations? The existence of such correlations forces much of the dynamics in this theory to be, in a certain sense, classical, with consequences for teleportation, cryptography, and computation. I also investigate another theory in which all states are local. Finally, I raise the question of what further axiom(s) could be added to the framework in order to identify quantum theory uniquely, and hypothesize that quantum theory is optimal for computation.
Unravelling the Social Network: Theory and Research
ERIC Educational Resources Information Center
Merchant, Guy
2012-01-01
Despite the widespread popularity of social networking sites (SNSs) amongst children and young people in compulsory education, relatively little scholarly work has explored the fundamental issues at stake. This paper makes an original contribution to the field by locating the study of this online activity within the broader terrain of social…
Realizing Wisdom Theory in Complex Learning Networks
ERIC Educational Resources Information Center
Kok, Ayse
2009-01-01
The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…
Complexity Theory and Network Centric Warfare
2003-09-01
Prediction ......................................... 60 Figure 3.4: 2nd Armoured Division– Neural Net Prediction...61 Figure 3.7: 9th Armoured Division– Neural Net Prediction........................................ 61 vi Figure 3.8...approach has been contrasted with three other prediction methodologies: a neural network; use of nonlinear prediction (a prediction is made by
Unravelling the Social Network: Theory and Research
ERIC Educational Resources Information Center
Merchant, Guy
2012-01-01
Despite the widespread popularity of social networking sites (SNSs) amongst children and young people in compulsory education, relatively little scholarly work has explored the fundamental issues at stake. This paper makes an original contribution to the field by locating the study of this online activity within the broader terrain of social…
An Information Processing Theory of Learning and Forgetting.
ERIC Educational Resources Information Center
Andre, Thomas
A theory of learning and forgetting is proposed which uses an information processing (IP) model. The IP model views learning as a process of storing, retrieving, and outputing information from a permanent memory. The concept of information pattern is important to the IP model because the pattern of information determines how the information will…
Information spread in networks: Games, optimal control, and stabilization
NASA Astrophysics Data System (ADS)
Khanafer, Ali
This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack
Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison
NASA Astrophysics Data System (ADS)
De Domenico, Manlio; Biamonte, Jacob
2016-10-01
Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.
NASA Astrophysics Data System (ADS)
Park, J.; Obeysekera, J.; Vanzee, R.
2005-05-01
The Management Simulation Engine (MSE) component of the Regional Simulation Model (RSM) incorporates a multi-level hierarchical control architecture which emphasizes the decoupling of hydrological state information from the management information processing applied to the states. A crucial aspect of effectively storing and accessing state information for water resource management purposes is the maintenance of an efficient storage mechanism which associates hydrological state information with the proper managerial abstractions. In the RSM this is done by storing hydrological and managerial information relevant to a water control unit (WCU) in a data storage object defined in the MSE Network. The MSE Network is an abstraction of the stream flow network and control structures suited to the needs of water resource routing and decisions. It is based on a standard graph theory representation of a flow network comprised of arcs and nodes. The MSE Network data objects serve as state and process information repositories for management processes. They maintain appropriately filtered state information, parameter storage relevant to WCU or hydraulic structure managerial constraints and variables, and serve as an integrated data source for any MSE algorithm. It also provides a mathematical representation of a constrained, interconnected flow network which facilitates efficient graph theory solutions of network connectivity and flow algorithms. This paper describes the MSE Network implementation in the RSM, an integrated hydrological computation engine aimed at meeting the needs for comprehensive integration of management features in coupled hydrological models [1]. [1] Belaineh, G., Peralta, R. C., Hughes, T. C., Simulation/ Optimization Modeling for Water Resources Management, ASCE Journal Water Resources Planning Management, 125(3), p 154-61, 1999
Information Theory Density Matrix for a Simple Quantum System.
ERIC Educational Resources Information Center
Titus, William J.
1979-01-01
Derives the density matrix that best describes, according to information theory, a one-dimensional single particle quantum system when the only information available is the values for the linear and quadratic position-momentum moments. (Author/GA)
USING INFORMATION THEORY TO DEFINE A SUSTAINABILITY INDEX
Information theory has many applications in Ecology and Environmental science, such as a biodiversity indicator, as a measure of evolution, a measure of distance from thermodynamic equilibrium, and as a measure of system organization. Fisher Information, in particular, provides a...
USING INFORMATION THEORY TO DEFINE A SUSTAINABILITY INDEX
Information theory has many applications in Ecology and Environmental science, such as a biodiversity indicator, as a measure of evolution, a measure of distance from thermodynamic equilibrium, and as a measure of system organization. Fisher Information, in particular, provides a...
Information Theory Density Matrix for a Simple Quantum System.
ERIC Educational Resources Information Center
Titus, William J.
1979-01-01
Derives the density matrix that best describes, according to information theory, a one-dimensional single particle quantum system when the only information available is the values for the linear and quadratic position-momentum moments. (Author/GA)
Dempster-Shafer information measures in category theory
NASA Astrophysics Data System (ADS)
Peri, Joseph S. J.
2016-05-01
In the Dempster Shafer context, one can construct new types of information measures based on belief and plausibility functions. These measures differ from those in Shannon's theory because, in his theory, information measures are based on probability functions. Other types of information measures were discovered by Kampe de Feriet and his colleagues in the French and Italian schools of mathematics. The objective of this paper is to construct a new category of information. I use category theory to construct a general setting in which the various types of information measures are special cases.
Applying Information Processing Theory to Supervision: An Initial Exploration
ERIC Educational Resources Information Center
Tangen, Jodi L.; Borders, L. DiAnne
2017-01-01
Although clinical supervision is an educational endeavor (Borders & Brown, [Borders, L. D., 2005]), many scholars neglect theories of learning in working with supervisees. The authors describe 1 learning theory--information processing theory (Atkinson & Shiffrin, 1968, 1971; Schunk, 2016)--and the ways its associated interventions may…
Hyland, Michael E
2003-12-01
Extended Network Generalized Entanglement Theory (Entanglement Theory for short) combines two earlier theories based on complexity theory and quantum mechanics. The theory's assumptions are: the body is a complex, self-organizing system (the extended network) that self-organizes so as to achieve genetically defined patterns (where patterns include morphologic as well as lifestyle patterns). These pattern-specifying genes require feedback that is provided by generalized quantum entanglement. Additionally, generalized entanglement has evolved as a form of communication between people (and animals) and can be used in healing. Entanglement Theory suggests that several processes are involved in complementary and alternative medicine (CAM). Direct subtle therapy creates network change either through lifestyle management, some manual therapies, and psychologically mediated effects of therapy. Indirect subtle therapy is a process of entanglement with other people or physical entities (e.g., remedies, healing sites). Both types of subtle therapy create two kinds of information within the network--either that the network is more disregulated than it is and the network then compensates for this error, or as a guide for network change leading to healing. Most CAM therapies involve a combination of indirect and direct therapies, making empirical evaluation complex. Empirical predictions from this theory are contrasted with those from two other possible mechanisms of healing: (1) psychologic processes and (2) mechanisms involving electromagnetic influence between people (biofield/energy medicine). Topics for empirical study include a hyperfast communication system, the phenomenology of entanglement, predictors of outcome in naturally occurring clinical settings, and the importance of therapist and patient characteristics to outcome.
78 FR 7797 - Homeland Security Information Network Advisory Committee (HSINAC)
Federal Register 2010, 2011, 2012, 2013, 2014
2013-02-04
... SECURITY Homeland Security Information Network Advisory Committee (HSINAC) AGENCY: OPS/OCIO, DHS. ACTION... Information Network Advisory Committee (HSIN AC) will meet on February 27th-28th, 2013 in Washington, DC. The... Network Advisory Committee), go to http://www.regulations.gov . Short public comment periods will be held...
Deep Space Network information system architecture study
NASA Technical Reports Server (NTRS)
Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.
1992-01-01
The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.
Deep Space Network information system architecture study
NASA Technical Reports Server (NTRS)
Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.
1992-01-01
The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.
VIOLIN: vaccine investigation and online information network.
Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P; Kovacic, Bethany L; Larson, Charles B; Chen, Fang; Hodges, Andrew P; Tian, Yuying; Olenzek, Elizabeth A; Zhao, Boyang; Colby, Lesley A; Rush, Howard G; Gilsdorf, Janet R; Jourdian, George W; He, Yongqun
2008-01-01
Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org.
A unified data representation theory for network visualization, ordering and coarse-graining.
Kovács, István A; Mizsei, Réka; Csermely, Péter
2015-09-08
Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.
Fidelity measure and conservation of information in general probabilistic theories
NASA Astrophysics Data System (ADS)
Zander, C.; Plastino, A. R.
2009-04-01
We investigate the main features of a measure of fidelity between states in a general family of probabilistic theories admitting classical probability theory and standard quantum theory as particular instances. We apply the aforementioned measure to investigate information-theoretical features of these theories related to the conservation of information during the evolution of closed physical systems. In particular, we derive a generalization of a fundamental result in quantum theory relevant for the measurement problem: Zurek's recent extension of the no-cloning theorem.
Training Records And Information Network UNIX Version
Johnston, Michael
1996-12-01
TRAIN-UNIX is used to track training requirements, qualifications, training completion and schedule training, classrooms and instructors. TRAIN-UNIX is a requirements-based system. When the identified training requirements for specific jobs are entered into the system, the employees manager or responsible training person assigns jobs to an employee. TRAIN-UNIX will then assemble an Individual Training Plan (ITP) with all courses required. ITP''s can also be modified to add any special training directed or identified by management, best business practices, procedures, etc. TRAIN-UNIX also schedules and tracks conferences, seminars, and required reading. TRAIN-UNIX is a secure database system on a server accessible via the network. Access to the user functions (scheduling, data entry, ITP modification etc.) within TRAIN-UNIX are granted by function, as needed, by the system administrator. An additional level of security allows those who access TRAIN-UNIX to only add, modify or view information for the organizations to which they belong. TRAIN-UNIX scheduling function allows network access to scheduling of students. As a function of the scheduling process, TRAIN-UNIX checks to insure that the student is a valid employee, not double booked, and the instructor and classroom are not double booked. TRAIN-UNIX will report pending lapse of courses or qualifications. This ability to know the lapse of training along with built in training requesting function allows the training deliverers to forecast training needs.
Parsimonious modeling with information filtering networks
NASA Astrophysics Data System (ADS)
Barfuss, Wolfram; Massara, Guido Previde; Di Matteo, T.; Aste, Tomaso
2016-12-01
We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided.
Parsimonious modeling with information filtering networks.
Barfuss, Wolfram; Massara, Guido Previde; Di Matteo, T; Aste, Tomaso
2016-12-01
We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided.
Nonlinear neural networks. II. Information processing
NASA Astrophysics Data System (ADS)
van Hemmen, J. L.; Grensing, D.; Huber, A.; Kühn, R.
1988-01-01
Information processing in nonlinear neural networks with a finite number q of stored patterns is studied. Each network is characterized completely by its synaptic kernel Q. At low temperatures, the nonlinearity typically results in 2q-2- q metastable, pure states in addition to the q retrieval states that are associated with the q stored patterns. These spurious states start appearing at a temperaturetilde T_q , which depends on q. We give sufficient conditions to guarantee that the retrieval states bifurcate first at a critical temperature T c and thattilde T_q / T c → 0 as q→∞. Hence, there is a large temperature range where only the retrieval states and certain symmetric mixtures thereof exist. The latter are unstable, as they appear at T c . For clipped synapses, the bifurcation and stability structure is analyzed in detail and shown to approach that of the (linear) Hopfield model as q→∞. We also investigate memories that forget and indicate how forgetfulness can be explained in terms of the eigenvalue spectrum of the synaptic kernel Q.
Improving clustering by imposing network information
Gerber, Susanne; Horenko, Illia
2015-01-01
Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface. PMID:26601225
Integrated condition monitoring of space information network
NASA Astrophysics Data System (ADS)
Wang, Zhilin; Li, Xinming; Li, Yachen; Yu, Shaolin
2015-11-01
In order to solve the integrated condition monitoring problem in space information network, there are three works finished including analyzing the characteristics of tasks process and system health monitoring, adopting the automata modeling method, and respectively establishing the models for state inference and state determination. The state inference model is a logic automaton and is gotten by concluding engineering experiences. The state determination model is a double-layer automaton, the lower automaton is responsible for parameter judge and the upper automaton is responsible for state diagnosis. At last, the system state monitoring algorithm has been proposed, which realizes the integrated condition monitoring for task process and system health, and can avoid the false alarm.
Bayesian information fusion networks for biosurveillance applications.
Mnatsakanyan, Zaruhi R; Burkom, Howard S; Coberly, Jacqueline S; Lombardo, Joseph S
2009-01-01
This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.
Business information query expansion through semantic network
NASA Astrophysics Data System (ADS)
Gong, Zhiguo; Muyeba, Maybin; Guo, Jingzhi
2010-02-01
In this article, we propose a method for business information query expansions. In our approach, hypernym/hyponymy and synonym relations in WordNet are used as the basic expansion rules. Then we use WordNet Lexical Chains and WordNet semantic similarity to assign terms in the same query into different groups with respect to their semantic similarities. For each group, we expand the highest terms in the WordNet hierarchies with hypernym and synonym, the lowest terms with hyponym and synonym and all other terms with only synonym. In this way, the contradictory caused by full expansion can be well controlled. Furthermore, we use collection-related term semantic network to further improve the expansion performance. And our experiment reveals that our solution for query expansion can improve the query performance dramatically.
Intra- Versus Intersex Aggression: Testing Theories of Sex Differences Using Aggression Networks.
Wölfer, Ralf; Hewstone, Miles
2015-08-01
Two theories offer competing explanations of sex differences in aggressive behavior: sexual-selection theory and social-role theory. While each theory has specific strengths and limitations depending on the victim's sex, research hardly differentiates between intrasex and intersex aggression. In the present study, 11,307 students (mean age = 14.96 years; 50% girls, 50% boys) from 597 school classes provided social-network data (aggression and friendship networks) as well as physical (body mass index) and psychosocial (gender and masculinity norms) information. Aggression networks were used to disentangle intra- and intersex aggression, whereas their class-aggregated sex differences were analyzed using contextual predictors derived from sexual-selection and social-role theories. As expected, results revealed that sexual-selection theory predicted male-biased sex differences in intrasex aggression, whereas social-role theory predicted male-biased sex differences in intersex aggression. Findings suggest the value of explaining sex differences separately for intra- and intersex aggression with a dual-theory framework covering both evolutionary and normative components.
Regional health information networks: the Wisconsin Health Information Network, a case study.
Pemble, K. R.
1994-01-01
It is projected that by the turn of the century, ninety percent of diagnostic procedures and seventy percent of therapeutic procedures will occur outside a hospital setting [2,3]. Additionally, according to a 1992 study by Arthur D. Little, during any given physician office visit, as much as 30 percent of the required diagnostic data and information required by the physician is unavailable [4]. Driven by ever increasing demands for convenience and accessibility, health care continues to evolve into an environment where the importance of data and its relative availability to the requester are diverging. This paper will present the concept of a regional or community health information network (RHIN or CHIN). Specifically, the Wisconsin Health Information Network (WHIN) will be used as a case study. PMID:7949958
Empirical Laws and Theories of Information and Software Sciences.
ERIC Educational Resources Information Center
Zunde, Pranas
1984-01-01
Reviews what information and software sciences have thus far accomplished in search for empirical regularities and laws and examines what theories have been developed to explain and account for regularities and laws. Specific laws and theories of information highlighted are those of Zipf, Bradford, Lotka, Mandelbrot, and Simon. (Forty references)…
Graph theory network function in Parkinson's disease assessed with electroencephalography.
Utianski, Rene L; Caviness, John N; van Straaten, Elisabeth C W; Beach, Thomas G; Dugger, Brittany N; Shill, Holly A; Driver-Dunckley, Erika D; Sabbagh, Marwan N; Mehta, Shyamal; Adler, Charles H; Hentz, Joseph G
2016-05-01
To determine what differences exist in graph theory network measures derived from electroencephalography (EEG), between Parkinson's disease (PD) patients who are cognitively normal (PD-CN) and matched healthy controls; and between PD-CN and PD dementia (PD-D). EEG recordings were analyzed via graph theory network analysis to quantify changes in global efficiency and local integration. This included minimal spanning tree analysis. T-tests and correlations were used to assess differences between groups and assess the relationship with cognitive performance. Network measures showed increased local integration across all frequency bands between control and PD-CN; in contrast, decreased local integration occurred in PD-D when compared to PD-CN in the alpha1 frequency band. Differences found in PD-MCI mirrored PD-D. Correlations were found between network measures and assessments of global cognitive performance in PD. Our results reveal distinct patterns of band and network measure type alteration and breakdown for PD, as well as with cognitive decline in PD. These patterns suggest specific ways that interaction between cortical areas becomes abnormal and contributes to PD symptoms at various stages. Graph theory analysis by EEG suggests that network alteration and breakdown are robust attributes of PD cortical dysfunction pathophysiology. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Complex network theory, streamflow, and hydrometric monitoring system design
NASA Astrophysics Data System (ADS)
Halverson, M. J.; Fleming, S. W.
2015-07-01
Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.
Social Network Theory in Engineering Education
NASA Astrophysics Data System (ADS)
Simon, Peter A.
Collaborative groups are important both in the learning environment of engineering education and, in the real world, the business of engineering design. Selecting appropriate individuals to form an effective group and monitoring a group's progress are important aspects of successful task performance. This exploratory study looked at using the concepts of cognitive social structures, structural balance, and centrality from social network analysis as well as the measures of emotional intelligence. The concepts were used to analyze potential team members to examine if an individual's ability to perceive emotion in others and the self and to use, understand, and manage those emotions are a factor in a group's performance. The students from a capstone design course in computer engineering were used as volunteer subjects. They were formed into groups and assigned a design exercise to determine whether and which of the above-mentioned tools would be effective in both selecting teams and predicting the quality of the resultant design. The results were inconclusive with the exception of an individual's ability to accurately perceive emotions. The instruments that were successful were the Self-Monitoring scale and the accuracy scores derived from cognitive social structures and Level IV of network levels of analysis.
Percolation theory applied to measures of fragmentation in social networks
NASA Astrophysics Data System (ADS)
Chen, Yiping; Paul, Gerald; Cohen, Reuven; Havlin, Shlomo; Borgatti, Stephen P.; Liljeros, Fredrik; Stanley, H. Eugene
2007-04-01
We apply percolation theory to a recently proposed measure of fragmentation F for social networks. The measure F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removing a fraction q of nodes and the total number of pairs in the original fully connected network. We compare F with the traditional measure used in percolation theory, P∞ , the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods from percolation, we study Erdős-Rényi and scale-free networks under various types of node removal strategies. The removal strategies are random removal, high degree removal, and high betweenness centrality removal. We find that for a network obtained after removal (all strategies) of a fraction q of nodes above percolation threshold, P∞≈(1-F)1/2 . For fixed P∞ and close to percolation threshold (q=qc) , we show that 1-F better reflects the actual fragmentation. Close to qc , for a given P∞ , 1-F has a broad distribution and it is thus possible to improve the fragmentation of the network. We also study and compare the fragmentation measure F and the percolation measure P∞ for a real social network of workplaces linked by the households of the employees and find similar results.
Application of Ecological Network Theory to the Human Microbiome
Foster, James A.; Krone, Stephen M.; Forney, Larry J.
2008-01-01
In healthy humans, many microbial consortia constitute rich ecosystems with dozens to hundreds of species, finely tuned to functions relevant to human health. Medical interventions, lifestyle changes, and the normal rhythms of life sometimes upset the balance in microbial ecosystems, facilitating pathogen invasions or causing other clinically relevant problems. Some diseases, such as bacterial vaginosis, have exactly this sort of community etiology. Mathematical network theory is ideal for studying the ecological networks of interacting species that comprise the human microbiome. Theoretical networks require little consortia specific data to provide insight into both normal and disturbed microbial community functions, but it is easy to incorporate additional empirical data as it becomes available. We argue that understanding some diseases, such as bacterial vaginosis, requires a shift of focus from individual bacteria to (mathematical) networks of interacting populations, and that known emergent properties of these networks will provide insights that would be otherwise elusive. PMID:19259330
Quantum Information Processing with Modular Networks
NASA Astrophysics Data System (ADS)
Crocker, Clayton; Inlek, Ismail V.; Hucul, David; Sosnova, Ksenia; Vittorini, Grahame; Monroe, Chris
2015-05-01
Trapped atomic ions are qubit standards for the production of entangled states in quantum information science and metrology applications. Trapped ions can exhibit very long coherence times, external fields can drive strong local interactions via phonons, and remote qubits can be entangled via photons. Transferring quantum information across spatially separated ion trap modules for a scalable quantum network architecture relies on the juxtaposition of both phononic and photonic buses. We report the successful combination of these protocols within and between two ion trap modules on a unit structure of this architecture where the remote entanglement generation rate exceeds the experimentally measured decoherence rate. Additionally, we report an experimental implementation of a technique to maintain phase coherence between spatially and temporally distributed quantum gate operations, a crucial prerequisite for scalability. Finally, we discuss our progress towards addressing the issue of uncontrolled cross-talk between photonic qubits and memory qubits by implementing a second ion species, Barium, to generate the photonic link. This work is supported by the ARO with funding from the IARPA MQCO program, the DARPA Quiness Program, the ARO MURI on Hybrid Quantum Circuits, the AFOSR MURI on Quantum Transduction, and the NSF Physics Frontier Center at JQI.
Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta
2017-01-01
Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic. PMID:28245222
Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta
2017-01-01
Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.
Information content and cross-talk in biological signal transduction: An information theory study
NASA Astrophysics Data System (ADS)
Prasad, Ashok; Lyons, Samanthe
2014-03-01
Biological cells respond to chemical cues provided by extra-cellular chemical signals, but many of these chemical signals and the pathways they activate interfere and overlap with one another. How well cells can distinguish between interfering extra-cellular signals is thus an important question in cellular signal transduction. Here we use information theory with stochastic simulations of networks to address the question of what happens to total information content when signals interfere. We find that both total information transmitted by the biological pathway, as well as its theoretical capacity to discriminate between overlapping signals, are relatively insensitive to cross-talk between the extracellular signals, until significantly high levels of cross-talk have been reached. This robustness of information content against cross-talk requires that the average amplitude of the signals are large. We predict that smaller systems, as exemplified by simple phosphorylation relays (two-component systems) in bacteria, should be significantly much less robust against cross-talk. Our results suggest that mammalian signal transduction can tolerate a high amount of cross-talk without degrading information content, while smaller bacterial systems cannot.
Dimension theory of graphs and networks
NASA Astrophysics Data System (ADS)
Nowotny, Thomas; Requardt, Manfred
1998-03-01
Starting from the working hypothesis that both physics and the corresponding mathematics have to be described by means of discrete concepts on the Planck scale, one of the many problems one has to face in this enterprise is to find the discrete protoforms of the building blocks of continuum physics and mathematics. A core concept is the notion of dimension. In the following we develop such a notion for irregular structures such as (large) graphs and networks and derive a number of its properties. Among other things we show its stability under a wide class of perturbations which is important if one has ` dimensional phase transitions' in mind. Furthermore we systematically construct graphs with almost arbitrary ` fractal dimension' which may be of some use in the context of ` dimensional renormalization' or statistical mechanics on irregular sets.
Theory of interface: category theory, directed networks and evolution of biological networks.
Haruna, Taichi
2013-11-01
Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.
The role of relative entropy in quantum information theory
NASA Astrophysics Data System (ADS)
Vedral, V.
2002-01-01
Quantum mechanics and information theory are among the most important scientific discoveries of the last century. Although these two areas initially developed separately, it has emerged that they are in fact intimately related. In this review the author shows how quantum information theory extends traditional information theory by exploring the limits imposed by quantum, rather than classical, mechanics on information storage and transmission. The derivation of many key results differentiates this review from the usual presentation in that they are shown to follow logically from one crucial property of relative entropy. Within the review, optimal bounds on the enhanced speed that quantum computers can achieve over their classical counterparts are outlined using information-theoretic arguments. In addition, important implications of quantum information theory for thermodynamics and quantum measurement are intermittently discussed. A number of simple examples and derivations, including quantum superdense coding, quantum teleportation, and Deutsch's and Grover's algorithms, are also included.
New scaling relation for information transfer in biological networks
Kim, Hyunju; Davies, Paul; Walker, Sara Imari
2015-01-01
We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883
Evolutionarily derived networks to inform disease pathways.
Graham, Britney E; Darabos, Christian; Huang, Minjun; Muglia, Louis J; Moore, Jason H; Williams, Scott M
2017-09-25
Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET). ET utilizes the hypothesis that alleles affecting disease risk in multiple populations are distributed consistently with differences in disease prevalence and compares allele frequencies among populations and their relationship to phenotype prevalence. We hypothesized that combining these methods will increase our ability to detect genetic patterns of association in complex diseases. We combined HPN and ET to identify network patterns associated with type 2 diabetes mellitus (T2DM), a leading cause of death worldwide. Fasting glucose, a continuous trait, was used as a proxy for T2DM and differs significantly among continental populations. The combined method identified several diabetes-related traits and several phenotypes related to cardiovascular diseases, for which diabetes is a major risk factor. ET-HPN found more phenotypes related to our target and related phenotypes than the application of either method alone. Not only could we detect phenotype connections related to T2DM, but we also identified phenotypes that are distributed in parallel to it, e.g., amyotrophic lateral sclerosis. Our analyses showed that ET-filtered HPN provides information that neither technique can individually. © 2017 WILEY PERIODICALS, INC.
Kinetic energy decomposition scheme based on information theory.
Imamura, Yutaka; Suzuki, Jun; Nakai, Hiromi
2013-12-15
We proposed a novel kinetic energy decomposition analysis based on information theory. Since the Hirshfeld partitioning for electron densities can be formulated in terms of Kullback-Leibler information deficiency in information theory, a similar partitioning for kinetic energy densities was newly proposed. The numerical assessments confirm that the current kinetic energy decomposition scheme provides reasonable chemical pictures for ionic and covalent molecules, and can also estimate atomic energies using a correction with viral ratios. Copyright © 2013 Wiley Periodicals, Inc.
Evaluating Action Learning: A Critical Realist Complex Network Theory Approach
ERIC Educational Resources Information Center
Burgoyne, John G.
2010-01-01
This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…
Organizational Application of Social Networking Information Technologies
ERIC Educational Resources Information Center
Reppert, Jeffrey R.
2012-01-01
The focus of this qualitative research study using the Delphi method is to provide a framework for leaders to develop their own social networks. By exploring concerns in four areas, leaders may be able to better plan, implement, and manage social networking systems in organizations. The areas addressed are: (a) social networking using…
Organizational Application of Social Networking Information Technologies
ERIC Educational Resources Information Center
Reppert, Jeffrey R.
2012-01-01
The focus of this qualitative research study using the Delphi method is to provide a framework for leaders to develop their own social networks. By exploring concerns in four areas, leaders may be able to better plan, implement, and manage social networking systems in organizations. The areas addressed are: (a) social networking using…
Can computational goals inform theories of vision?
Anderson, Barton L
2015-04-01
One of the most lasting contributions of Marr's posthumous book is his articulation of the different "levels of analysis" that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions. Marr placed particular significance on computational level theory, which specifies the "goal" of a computation, its appropriateness for solving a particular problem, and the logic by which it can be carried out. The structure of computational level theory is inherently teleological: What the brain does is described in terms of its purpose. I argue that computational level theory, and the reverse-engineering approach it inspires, requires understanding the historical trajectory that gave rise to functional capacities that can be meaningfully attributed with some sense of purpose or goal, that is, a reconstruction of the fitness function on which natural selection acted in shaping our visual abilities. I argue that this reconstruction is required to distinguish abilities shaped by natural selection-"natural tasks" -from evolutionary "by-products" (spandrels, co-optations, and exaptations), rather than merely demonstrating that computational goals can be embedded in a Bayesian model that renders a particular behavior or process rational. Copyright © 2015 Cognitive Science Society, Inc.
The use of information theory in evolutionary biology.
Adami, Christoph
2012-05-01
Information is a key concept in evolutionary biology. Information stored in a biological organism's genome is used to generate the organism and to maintain and control it. Information is also that which evolves. When a population adapts to a local environment, information about this environment is fixed in a representative genome. However, when an environment changes, information can be lost. At the same time, information is processed by animal brains to survive in complex environments, and the capacity for information processing also evolves. Here, I review applications of information theory to the evolution of proteins and to the evolution of information processing in simulated agents that adapt to perform a complex task.
From information theory to quantitative description of steric effects.
Alipour, Mojtaba; Safari, Zahra
2016-07-21
Immense efforts have been made in the literature to apply the information theory descriptors for investigating the electronic structure theory of various systems. In the present study, the information theoretic quantities, such as Fisher information, Shannon entropy, Onicescu information energy, and Ghosh-Berkowitz-Parr entropy, have been used to present a quantitative description for one of the most widely used concepts in chemistry, namely the steric effects. Taking the experimental steric scales for the different compounds as benchmark sets, there are reasonable linear relationships between the experimental scales of the steric effects and theoretical values of steric energies calculated from information theory functionals. Perusing the results obtained from the information theoretic quantities with the two representations of electron density and shape function, the Shannon entropy has the best performance for the purpose. On the one hand, the usefulness of considering the contributions of functional groups steric energies and geometries, and on the other hand, dissecting the effects of both global and local information measures simultaneously have also been explored. Furthermore, the utility of the information functionals for the description of steric effects in several chemical transformations, such as electrophilic and nucleophilic reactions and host-guest chemistry, has been analyzed. The functionals of information theory correlate remarkably with the stability of systems and experimental scales. Overall, these findings show that the information theoretic quantities can be introduced as quantitative measures of steric effects and provide further evidences of the quality of information theory toward helping theoreticians and experimentalists to interpret different problems in real systems.
Theory VI. Computational Materials Sciences Network (CMSN)
Zhang, Z Y
2008-06-25
The Computational Materials Sciences Network (CMSN) is a virtual center consisting of scientists interested in working together, across organizational and disciplinary boundaries, to formulate and pursue projects that reflect challenging and relevant computational research in the materials sciences. The projects appropriate for this center involve those problems best pursued through broad cooperative efforts, rather than those key problems best tackled by single investigator groups. CMSN operates similarly to the DOE Center of Excellence for the Synthesis and Processing of Advanced Materials, coordinated by George Samara at Sandia. As in the Synthesis and Processing Center, the intent of the modest funding for CMSN is to foster partnering and collective activities. All CMSN proposals undergo external peer review and are judged foremost on the quality and timeliness of the science and also on criteria relevant to the objective of the center, especially concerning a strategy for partnering. More details about CMSN can be found on the CMSN webpages at: http://cmpweb.ameslab.gov/ccms/CMSN-homepage.html.
Analysis of the enzyme network involved in cattle milk production using graph theory.
Ghorbani, Sholeh; Tahmoorespur, Mojtaba; Masoudi Nejad, Ali; Nasiri, Mohammad; Asgari, Yazdan
2015-06-01
Understanding cattle metabolism and its relationship with milk products is important in bovine breeding. A systemic view could lead to consequences that will result in a better understanding of existing concepts. Topological indices and quantitative characterizations mostly result from the application of graph theory on biological data. In the present work, the enzyme network involved in cattle milk production was reconstructed and analyzed based on available bovine genome information using several public datasets (NCBI, Uniprot, KEGG, and Brenda). The reconstructed network consisted of 3605 reactions named by KEGG compound numbers and 646 enzymes that catalyzed the corresponding reactions. The characteristics of the directed and undirected network were analyzed using Graph Theory. The mean path length was calculated to be4.39 and 5.41 for directed and undirected networks, respectively. The top 11 hub enzymes whose abnormality could harm bovine health and reduce milk production were determined. Therefore, the aim of constructing the enzyme centric network was twofold; first to find out whether such network followed the same properties of other biological networks, and second, to find the key enzymes. The results of the present study can improve our understanding of milk production in cattle. Also, analysis of the enzyme network can help improve the modeling and simulation of biological systems and help design desired phenotypes to increase milk production quality or quantity.
Inferring Boolean network states from partial information
2013-01-01
Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpretation of the measurements is not straightforward and since the data contain noise. In order to facilitate a more reliable mapping between datasets and Boolean networks, we develop an algorithm that infers network trajectories from a dataset distorted by noise. We analyze our algorithm theoretically and demonstrate its accuracy using simulation and microarray expression data. PMID:24006954
Analysis and improvement of vehicle information sharing networks
NASA Astrophysics Data System (ADS)
Gong, Hang; He, Kun; Qu, Yingchun; Wang, Pu
2016-06-01
Based on large-scale mobile phone data, mobility demand was estimated and locations of vehicles were inferred in the Boston area. Using the spatial distribution of vehicles, we analyze the vehicle information sharing network generated by the vehicle-to-vehicle (V2V) communications. Although a giant vehicle cluster is observed, the coverage and the efficiency of the information sharing network remain limited. Consequently, we propose a method to extend the information sharing network's coverage by adding long-range connections between targeted vehicle clusters. Furthermore, we employ the optimal design strategy discovered in square lattice to improve the efficiency of the vehicle information sharing network.
Electronic Information and Applications in Musicology and Music Theory.
ERIC Educational Resources Information Center
Duggan, Mary Kay
1992-01-01
Describes electronic publishing and information resources in the field of music. Topics addressed include bibliographic citations of books, journal articles, scores, and sound recordings; bibliographic utilities; computer network resources; electronic music applications; tutorial and laboratory projects; interactive multimedia publications; and…
Electronic Information and Applications in Musicology and Music Theory.
ERIC Educational Resources Information Center
Duggan, Mary Kay
1992-01-01
Describes electronic publishing and information resources in the field of music. Topics addressed include bibliographic citations of books, journal articles, scores, and sound recordings; bibliographic utilities; computer network resources; electronic music applications; tutorial and laboratory projects; interactive multimedia publications; and…
Community readiness for a computer-based health information network.
Ervin, Naomi E; Berry, Michelle M
2006-01-01
The need for timely and accurate communication among healthcare providers has prompted the development of computer-based health information networks that allow patient and client information to be shared among agencies. This article reports the findings of a study to assess whether residents of an upstate New York community were ready for a computer-based health information network to facilitate delivery of long term care services. Focus group sessions, which involved both consumers and professionals, revealed that security of personal information was of concern to healthcare providers, attorneys, and consumers. Physicians were the most enthusiastic about the possibility of a computer-based health information network. Consumers and other healthcare professionals, including nurses, indicated that such a network would be helpful to them personally. Nurses and other healthcare professionals need to be knowledgeable about the use of computer-based health information networks and other electronic information systems as this trend continues to spread across the U.S.
How mirror-touch informs theories of synesthesia.
Meier, Beat; Lunke, Katrin; Rothen, Nicolas
2015-01-01
Ward and Banissy provide an excellent overview of the state of mirror-touch research in order to advance this field. They present a comparison of two prominent theoretical approaches for understanding mirror-touch phenomena. According to the threshold theory, the phenomena arise as a result of a hyperactive mirror neuron system. According to the Self-Other Theory, they are due to disturbances in the ability to distinguish the self from others. Here, we explore how these two theories can inform theories of synesthesia more generally. We conclude that both theories are not suited as general models of synesthesia.
Theory of rumour spreading in complex social networks
NASA Astrophysics Data System (ADS)
Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M.
2007-01-01
We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.
How Fast Can Networks Synchronize? A Random Matrix Theory Approach
NASA Astrophysics Data System (ADS)
Timme, Marc; Wolf, Fred; Geisel, Theo
2004-03-01
Pulse-coupled oscillators constitute a paradigmatic class of dynamical systems interacting on networks because they model a variety of biological systems including flashing fireflies and chirping crickets as well as pacemaker cells of the heart and neural networks. Synchronization is one of the most simple and most prevailing kinds of collective dynamics on such networks. Here we study collective synchronization [1] of pulse-coupled oscillators interacting on asymmetric random networks. Using random matrix theory we analytically determine the speed of synchronization in such networks in dependence on the dynamical and network parameters [2]. The speed of synchronization increases with increasing coupling strengths. Surprisingly, however, it stays finite even for infinitely strong interactions. The results indicate that the speed of synchronization is limited by the connectivity of the network. We discuss the relevance of our findings to general equilibration processes on complex networks. [5mm] [1] M. Timme, F. Wolf, T. Geisel, Phys. Rev. Lett. 89:258701 (2002). [2] M. Timme, F. Wolf, T. Geisel, cond-mat/0306512 (2003).
Percolation theory and fragmentation measures in social networks
NASA Astrophysics Data System (ADS)
Chen, Yiping; Paul, Gerald; Cohen, Reuven; Havlin, Shlomo; Borgatti, Stephen P.; Liljeros, Fredrik; Eugene Stanley, H.
2007-05-01
We study the statistical properties of a recently proposed social networks measure of fragmentation F after removal of a fraction q of nodes or links from the network. The measure F is defined as the ratio of the number of pairs of nodes that are not connected in the fragmented network to the total number of pairs in the original fully connected network. We compare this measure with the one traditionally used in percolation theory, P∞, the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods, we study Erdős-Rényi (ER) and scale-free (SF) networks under various node removal strategies. We find that for a network obtained after removal of a fraction q of nodes above criticality, P∞≈(1-F). For fixed P∞ and close to criticality, we show that 1-F better reflects the actual fragmentation. For a given P∞, 1-F has a broad distribution and thus one can improve significantly the fragmentation of the network. We also study and compare the fragmentation measure F and the percolation measure P∞ for a real national social network of workplaces linked by the households of the employees and find similar results.
NASA Astrophysics Data System (ADS)
Pahlavani, Parham; Sheikhian, Hossein; Bigdeli, Behnaz
2017-10-01
Air pollution assessment is an imperative part of megacities planning and control. Hence, a new comprehensive approach for air pollution monitoring and assessment was introduced in this research. It comprises of three main sections: optimizing the existing air pollutant monitoring network, locating new stations to complete the coverage of the existing network, and finally, generating an air pollution map. In the first section, Shannon information index was used to find less informative stations to be candidate for removal. Then, a methodology was proposed to determine the areas which are not sufficiently covered by the current network. These areas are candidates for establishing new monitoring stations. The current air pollution monitoring network of Tehran was used as a case study, where the air pollution issue has been worsened due to the huge population, considerable commuters' absorption and topographic barriers. In this regard, O3, NO, NO2, NOx, CO, PM10, and PM2.5 were considered as the main pollutants of Tehran. Optimization step concluded that all the 16 active monitoring stations should be preserved. Analysis showed that about 35% of the Tehran's area is not properly covered by monitoring stations and about 30% of the area needs additional stations. The winter period in Tehran always faces the most severe air pollution in the year. Hence, to produce the air pollution map of Tehran, three-month of winter measurements of the mentioned pollutants, repeated for five years in the same period, were selected and extended to the entire area using the kriging method. Experts specified the contribution of each pollutant in overall air pollution. Experts' rankings aggregated by a fuzzy-overlay process. Resulted maps characterized the study area with crucial air pollution situation. According to the maps, more than 45% of the city area faced high pollution in the study period, while only less than 10% of the area showed low pollution. This situation confirms the need
Jacobi, Martin Nilsson; Jonsson, Per R
2011-07-01
Conservation and management of natural resources and biodiversity need improved criteria to select functional networks of protected areas. The connectivity within networks due to dispersal is rarely considered, partly because it is unclear how connectivity information can be included in the selection of protected areas. We present a novel and general method that applies eigenvalue perturbation theory (EPT) to select optimum networks of protected areas based on connectivity. At low population densities, characteristic of threatened populations, this procedure selects networks that maximize the growth rate of the overall network. This method offers an improved link between connectivity and metapopulation dynamics. Our framework is applied to connectivities estimated for marine larvae and demonstrates that, for open populations, the best strategy is to protect areas acting as both strong donors and recipients of recruits. It should be possible to implement an EPT framework for connectivity analysis into existing holistic tools for design of protected areas.
Information diversity in structure and dynamics of simulated neuronal networks.
Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena
2011-01-01
Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
Information integration based predictions about the conscious states of a spiking neural network.
Gamez, David
2010-03-01
This paper describes how Tononi's information integration theory of consciousness was used to make detailed predictions about the distribution of phenomenal states in a spiking neural network. This network had approximately 18,000 neurons and 700,000 connections and it used models of emotion and imagination to control the eye movements of a virtual robot and avoid 'negative' stimuli. The first stage in the analysis was the development of a formal definition of Tononi's theory of consciousness. The network was then analysed for information integration and detailed predictions were made about the distribution of consciousness for each time step of recorded activity. This work demonstrates how an artificial system can be analysed for consciousness using a particular theory and in the future this approach could be used to make predictions about the phenomenal states associated with biological systems.
Influence Function Learning in Information Diffusion Networks
Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le
2015-01-01
Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data. PMID:25973445
Wang, Xin; Wang, Ying; Sun, Hongbin
2016-01-01
In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.
Wang, Xin; Wang, Ying; Sun, Hongbin
2016-01-01
In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework. PMID:27034651
REGIME CHANGES IN ECOLOGICAL SYSTEMS: AN INFORMATION THEORY APPROACH
We present our efforts at developing an ecological system using Information Theory. We derive an expression for Fisher Information based on sampling of the system trajectory as it evolves in the state space. The Fisher Information index as we have derived it captures the characte...
REGIME CHANGES IN ECOLOGICAL SYSTEMS: AN INFORMATION THEORY APPROACH
We present our efforts at developing an ecological system using Information Theory. We derive an expression for Fisher Information based on sampling of the system trajectory as it evolves in the state space. The Fisher Information index as we have derived it captures the characte...
PREFACE: Complex Networks: from Biology to Information Technology
NASA Astrophysics Data System (ADS)
Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.
2008-06-01
The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm
2016-06-01
18 Figure 2. The Cost of Cyber Crime ...........................................................................31...Figure 3. Cost Framework for Cyber Crime .............................................................33 Figure 4. Activity Cost Comparison and the...DOD Cyber Crime Center DCISE DIB Collaborative Information Sharing Environment DCR DOTMLPF-P Change Request DFARS Defense Acquisition
TOWARDS A SUSTAINABILITY INDEX USING INFORMATION THEORY
We explore the use of Fisher Information as a basis for an index of sustainability. Sustainability of an ecosystem refers to the robustness of a preferred dynamic regime to human and natural disturbances. Ecosystems under perturbations of varying regularity and intensity can ei...
Objectivism in Information Utilization: Theory and Measurement.
ERIC Educational Resources Information Center
Leary, Mark R.; And Others
A self-report scale was constructed and validated that measures individual differences in objectivism--the tendency to base one's judgments and beliefs upon empirical information and rational considerations. Validity data showed that, compared to people who score low on the Objectivism Scale, highly objective individuals enjoy thinking more, rely…
TOWARDS A SUSTAINABILITY INDEX USING INFORMATION THEORY
We explore the use of Fisher Information as a basis for an index of sustainability. Sustainability of an ecosystem refers to the robustness of a preferred dynamic regime to human and natural disturbances. Ecosystems under perturbations of varying regularity and intensity can ei...
Discovery of Information Diffusion Process in Social Networks
NASA Astrophysics Data System (ADS)
Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun
Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.
Information dynamics in small-world Boolean networks.
Lizier, Joseph T; Pritam, Siddharth; Prokopenko, Mikhail
2011-01-01
Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Reactivity Network: Secondary Sources for Inorganic Reactivity Information.
ERIC Educational Resources Information Center
Mellon, E. K.
1989-01-01
Provides an eclectic annotated bibliography of secondary sources for inorganic reactivity information of interest to reactivity network review authors and to anyone seeking information about simple inorganic reactions in order to develop experiments and demonstrations. Gives 119 sources. (MVL)
Reactivity Network: Secondary Sources for Inorganic Reactivity Information.
ERIC Educational Resources Information Center
Mellon, E. K.
1989-01-01
Provides an eclectic annotated bibliography of secondary sources for inorganic reactivity information of interest to reactivity network review authors and to anyone seeking information about simple inorganic reactions in order to develop experiments and demonstrations. Gives 119 sources. (MVL)
Extracting spatial information from networks with low-order eigenvectors
NASA Astrophysics Data System (ADS)
Cucuringu, Mihai; Blondel, Vincent D.; Van Dooren, Paul
2013-03-01
We consider the problem of inferring meaningful spatial information in networks from incomplete information on the connection intensity between the nodes of the network. We consider two spatially distributed networks: a population migration flow network within the US, and a network of mobile phone calls between cities in Belgium. For both networks we use the eigenvectors of the Laplacian matrix constructed from the link intensities to obtain informative visualizations and capture natural geographical subdivisions. We observe that some low-order eigenvectors localize very well and seem to reveal small geographically cohesive regions that match remarkably well with political and administrative boundaries. We discuss possible explanations for this observation by describing diffusion maps and localized eigenfunctions. In addition, we discuss a possible connection with the weighted graph cut problem, and provide numerical evidence supporting the idea that lower-order eigenvectors point out local cuts in the network. However, we do not provide a formal and rigorous justification for our observations.
Chemical reaction network approaches to Biochemical Systems Theory.
Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R
2015-11-01
This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. Copyright © 2015 Elsevier Inc. All rights reserved.
Optimal multi-community network modularity for information diffusion
NASA Astrophysics Data System (ADS)
Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong
2016-02-01
Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.
Ribeiro, João; Silva, Pedro; Duarte, Ricardo; Davids, Keith; Garganta, Júlio
2017-02-15
This paper discusses how social network analyses and graph theory can be implemented in team sports performance analyses to evaluate individual (micro) and collective (macro) performance data, and how to use this information for designing practice tasks. Moreover, we briefly outline possible limitations of social network studies and provide suggestions for future research. Instead of cataloguing discrete events or player actions, it has been argued that researchers need to consider the synergistic interpersonal processes emerging between teammates in competitive performance environments. Theoretical assumptions on team coordination prompted the emergence of innovative, theoretically driven methods for assessing collective team sport behaviours. Here, we contribute to this theoretical and practical debate by re-conceptualising sports teams as complex social networks. From this perspective, players are viewed as network nodes, connected through relevant information variables (e.g. a ball-passing action), sustaining complex patterns of interaction between teammates (e.g. a ball-passing network). Specialised tools and metrics related to graph theory could be applied to evaluate structural and topological properties of interpersonal interactions of teammates, complementing more traditional analysis methods. This innovative methodology moves beyond the use of common notation analysis methods, providing a richer understanding of the complexity of interpersonal interactions sustaining collective team sports performance. The proposed approach provides practical applications for coaches, performance analysts, practitioners and researchers by establishing social network analyses as a useful approach for capturing the emergent properties of interactions between players in sports teams.
Information theories for time-dependent harmonic oscillator
Choi, Jeong Ryeol; Kim, Min-Soo; Kim, Daeyeoul; Maamache, Mustapha; Menouar, Salah; Nahm, In Hyun
2011-06-15
Highlights: > Information theories for the general time-dependent harmonic oscillator based on invariant operator method. > Time dependence of entropies and entropic uncertainty relation. > Characteristics of Shannon information and Fisher information. > Application of information theories to particular systems that have time-dependent behavior. - Abstract: Information theories for the general time-dependent harmonic oscillator are described on the basis of invariant operator method. We obtained entropic uncertainty relation of the system and discussed whether it is always larger than or equal to the physically allowed minimum value. Shannon information and Fisher information are derived by means of density operator that satisfies Liouville-von Neumann equation and their characteristics are investigated. Shannon information is independent of time, but Fisher information is explicitly dependent on time as the time functions of the Hamiltonian vary. We can regard that the Fisher information is a local measure since its time behavior is largely affected by local arrangements of the density, whilst the Shannon information plays the role of a global measure of the spreading of density. To promote the understanding, our theory is applied to special systems, the so-called quantum oscillator with time-dependent frequency and strongly pulsating mass system.
Response to Patrick Love's "Informal Theory": A Rejoinder
ERIC Educational Resources Information Center
Evans, Nancy J.; Guido, Florence M.
2012-01-01
This rejoinder to Patrick Love's article, "Informal Theory: The Ignored Link in Theory-to-Practice," which appears earlier in this issue of the "Journal of College Student Development", was written at the invitation of the Editor. In the critique, we point out the weaknesses of many of Love's arguments and propositions. We provide an alternative…
Response to Patrick Love's "Informal Theory": A Rejoinder
ERIC Educational Resources Information Center
Evans, Nancy J.; Guido, Florence M.
2012-01-01
This rejoinder to Patrick Love's article, "Informal Theory: The Ignored Link in Theory-to-Practice," which appears earlier in this issue of the "Journal of College Student Development", was written at the invitation of the Editor. In the critique, we point out the weaknesses of many of Love's arguments and propositions. We provide an alternative…
Nonlinear effective-medium theory of disordered spring networks.
Sheinman, M; Broedersz, C P; MacKintosh, F C
2012-02-01
Disordered soft materials, such as fibrous networks in biological contexts, exhibit a nonlinear elastic response. We study such nonlinear behavior with a minimal model for networks on lattice geometries with simple Hookian elements with disordered spring constant. By developing a mean-field approach to calculate the differential elastic bulk modulus for the macroscopic network response of such networks under large isotropic deformations, we provide insight into the origins of the strain stiffening and softening behavior of these systems. We find that the nonlinear mechanics depends only weakly on the lattice geometry and is governed by the average network connectivity. In particular, the nonlinear response is controlled by the isostatic connectivity, which depends strongly on the applied strain. Our predictions for the strain dependence of the isostatic point as well as the strain-dependent differential bulk modulus agree well with numerical results in both two and three dimensions. In addition, by using a mapping between the disordered network and a regular network with random forces, we calculate the nonaffine fluctuations of the deformation field and compare them to the numerical results. Finally, we discuss the limitations and implications of the developed theory.
Modeling and dynamical topology properties of VANET based on complex networks theory
Zhang, Hong; Li, Jie
2015-01-15
Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a
Modeling and dynamical topology properties of VANET based on complex networks theory
NASA Astrophysics Data System (ADS)
Zhang, Hong; Li, Jie
2015-01-01
Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What's more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a
Informed consent in Texas: theory and practice.
Cherry, Mark J; Engelhardt, H Tristram
2004-04-01
The legal basis of informed consent in Texas may on first examination suggest an unqualified affirmation of persons as the source of authority over themselves. This view of individuals in the practice of informed consent tends to present persons outside of any social context in general and outside of their families in particular. The actual functioning of law and medical practice in Texas, however, is far more complex. This study begins with a brief overview of the roots of Texas law and public policy regarding informed consent. This surface account is then contrasted with examples drawn from the actual functioning of Texas law: Texas legislation regarding out-of-hospital do-not-resuscitate (DNR) orders. As a default approach to medical decision-making when patients lose decisional capacity and have failed to appoint a formal proxy or establish their wishes, this law establishes a defeasible presumption in favor of what the law characterizes as "qualified relatives" who can function as decision-makers for those terminal family members who lose decisional capacity. The study shows how, in the face of a general affirmation of the autonomy of individuals as if they were morally and socially isolated agents, space is nevertheless made for families to choose on behalf of their own members. The result is a multi-tier public morality, one affirming individuals as morally authoritative and the other recognizing the decisional standing of families.
The Elderly and Their Informal Social Networks.
ERIC Educational Resources Information Center
Thompson, J. Victor
1989-01-01
A sample of 334 people aged 56 and older living in British Columbia were interviewed about their supportive social network. Four supportive roles were investigated: caretaker, helper, confident, and advisor. The research supports earlier findings about the vulnerability of widows over 74 years old. They are most in need of networks. (Author/JOW)
Knowledge Communities and Information Network Policies.
ERIC Educational Resources Information Center
Young, Peter R.
The growing convergence of research library functions with a knowledge network infrastructure is beginning to have a transforming influence on the conduct of university research and instruction. Computing and communication network technologies are forcing a re-examination of institutional missions, the policies that support knowledge transmission,…
Twenty Years of Delila and Molecular Information Theory
Schneider, Thomas D.
2007-01-01
A brief personal history is given about how information theory can be applied to binding sites of genetic control molecules on nucleic acids. The primary example used is ribosome binding sites in Escherichia coli. Once the sites are aligned, the information needed to describe the sites can be computed using Claude Shannon’s method. This is displayed by a computer graphic called a sequence logo. The logo represents an average binding site, and the mathematics easily allows one to determine the components of this average. That is, given a set of binding sites, the information for individual binding sites can also be computed. One can go further and predict the information of sites that are not in the original data set. Information theory also allows one to model the flexibility of ribosome binding sites, and this led us to a simple model for ribosome translational initiation in which the molecular components fit together only when the ribosome is at a good ribosome binding site. Since information theory is general, the same mathematics applies to human splice junctions, where we can predict the effect of sequence changes that cause human genetic diseases and cancer. The second example given is the Pribnow ‘box’ which, when viewed by the information theory method, reveals a mechanism for initiation of both transcription and DNA replication. Replication, transcription, splicing, and translation into protein represent the central dogma, so these examples show how molecular information theory is contributing to our knowledge of basic biology. PMID:18084638
The Role of the Australian Open Learning Information Network.
ERIC Educational Resources Information Center
Bishop, Robin; And Others
Three documents are presented which describe the Australian Open Learning Information Network (AOLIN)--a national, independent, and self-supporting network of educational researchers with a common interest in the use of information technology for open and distance education--and discuss two evaluative studies undertaken by the organization. The…
Blending Formal and Informal Learning Networks for Online Learning
ERIC Educational Resources Information Center
Czerkawski, Betül C.
2016-01-01
With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…
Weather information network including graphical display
NASA Technical Reports Server (NTRS)
Leger, Daniel R. (Inventor); Burdon, David (Inventor); Son, Robert S. (Inventor); Martin, Kevin D. (Inventor); Harrison, John (Inventor); Hughes, Keith R. (Inventor)
2006-01-01
An apparatus for providing weather information onboard an aircraft includes a processor unit and a graphical user interface. The processor unit processes weather information after it is received onboard the aircraft from a ground-based source, and the graphical user interface provides a graphical presentation of the weather information to a user onboard the aircraft. Preferably, the graphical user interface includes one or more user-selectable options for graphically displaying at least one of convection information, turbulence information, icing information, weather satellite information, SIGMET information, significant weather prognosis information, and winds aloft information.
Information Processing in Social Insect Networks
Waters, James S.; Fewell, Jennifer H.
2012-01-01
Investigating local-scale interactions within a network makes it possible to test hypotheses about the mechanisms of global network connectivity and to ask whether there are general rules underlying network function across systems. Here we use motif analysis to determine whether the interactions within social insect colonies resemble the patterns exhibited by other animal associations or if they exhibit characteristics of biological regulatory systems. Colonies exhibit a predominance of feed-forward interaction motifs, in contrast to the densely interconnected clique patterns that characterize human interaction and animal social networks. The regulatory motif signature supports the hypothesis that social insect colonies are shaped by selection for network patterns that integrate colony functionality at the group rather than individual level, and demonstrates the utility of this approach for analysis of selection effects on complex systems across biological levels of organization. PMID:22815740
Phase response theory extended to nonoscillatory network components
Sieling, Fred H.; Archila, Santiago; Hooper, Ryan; Canavier, Carmen C.; Prinz, Astrid A.
2012-01-01
New tools for analysis of oscillatory networks using phase response theory (PRT) under the assumption of pulsatile coupling have been developed steadily since the 1980s, but none have yet allowed for analysis of mixed systems containing nonoscillatory elements. This caveat has excluded the application of PRT to most real systems, which are often mixed. We show that a recently developed tool, the functional phase resetting curve (fPRC), provides a serendipitous benefit: it allows incorporation of nonoscillatory elements into systems of oscillators where PRT can be applied. We validate this method in a model system of neural oscillators and a biological system, the pyloric network of crustacean decapods. PMID:23004844
Information Flow Between Resting-State Networks
Diez, Ibai; Erramuzpe, Asier; Escudero, Iñaki; Mateos, Beatriz; Cabrera, Alberto; Marinazzo, Daniele; Sanz-Arigita, Ernesto J.; Stramaglia, Sebastiano
2015-01-01
Abstract The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method—addressing differences in IF between RSNs for any generic data—can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. controls. The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls. PMID:26177254
Network selection, Information filtering and Scalable computation
NASA Astrophysics Data System (ADS)
Ye, Changqing
This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high dimensional structured data, where predictors corresponds to nodes of a given directed graph. This arises in, for instance, identification of disease genes for the Parkinson's diseases from a network of candidate genes. In such a situation, directed graph describes dependencies among the genes, where direction of edges represent certain causal effects. Key to high-dimensional structured classification and regression is how to utilize dependencies among predictors as specified by directions of the graph. In this dissertation, we develop a novel method that fully takes into account such dependencies formulated through certain nonlinear constraints. We apply the proposed method to two applications, feature selection in large margin binary classification and in linear regression. We implement the proposed method through difference convex programming for the cost function and constraints. Finally, theoretical and numerical analyses suggest that the proposed method achieves the desired objectives. An application to disease gene identification is presented. The second application scenario is personalized information filtering which extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we introduce novel partial latent models to integrate additional user-specific and content-specific predictors, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each representing a user's preference and an item preference by users. Then we propose a likelihood method to seek a sparsest latent factorization, from a class of over
Surfactant self-assembly in oppositely charged polymer networks. Theory.
Hansson, Per
2009-10-01
The interaction of ionic surfactants with polyion networks of opposite charge in an aqueous environment is analyzed theoretically by applying a recent theory of surfactant ion-polyion complex salts (J. Colloid. Int. Sci. 2009, 332, 183). The theory takes into account attractive and repulsive polyion-mediated interactions between the micelles, the deformation of the polymer network, the mixing of micelles, polyion chains, and simple ions with water, and the hydrophobic free energy at the micelle surface. The theory is used to calculate binding isotherms, swelling isotherms, surfactant aggregation numbers, compositions of complexes,and phase structure under various conditions. Factors controlling the gel volume transition and conditions for core/shell phase coexistence are investigated in detail, as well as the influence of salt. In particular, the interplay between electrostatic and elastic interactions is highlighted. Results from theory are compared with experimental data reported in the literature. The agreement is found to be semiquantitative or qualitative. The theory explains both the discrete volume transition observed in systems where the surfactant is in excess over the polyion and the core/shell phase coexistence in systems where the polyion is in excess.
Information Diffusion in Facebook-Like Social Networks Under Information Overload
NASA Astrophysics Data System (ADS)
Li, Pei; Xing, Kai; Wang, Dapeng; Zhang, Xin; Wang, Hui
2013-07-01
Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.
NASA Astrophysics Data System (ADS)
Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke
2017-05-01
Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.
Integrated information theory: from consciousness to its physical substrate.
Tononi, Giulio; Boly, Melanie; Massimini, Marcello; Koch, Christof
2016-07-01
In this Opinion article, we discuss how integrated information theory accounts for several aspects of the relationship between consciousness and the brain. Integrated information theory starts from the essential properties of phenomenal experience, from which it derives the requirements for the physical substrate of consciousness. It argues that the physical substrate of consciousness must be a maximum of intrinsic cause-effect power and provides a means to determine, in principle, the quality and quantity of experience. The theory leads to some counterintuitive predictions and can be used to develop new tools for assessing consciousness in non-communicative patients.
Applications of small-world network theory in alcohol epidemiology.
Braun, Richard J; Wilson, Robert A; Pelesko, John A; Buchanan, J Robert; Gleeson, James P
2006-07-01
This study develops a mathematical model of alcohol abuse in structured populations, such as communities and college campuses. The study employs a network model that has the capacity to incorporate a variety of forms of connectivity membership besides personal acquaintance, such as geographic proximity and common organizations. The model also incorporates a resilience dimension that indicates the susceptibility of each individual in a network to alcohol abuse. The model has the capacity to simulate the effect of moving alcohol abusers into networks of nonabusers, either as the result of treatment or membership in self-help organizations. The study employs a small-world model. A cubic equation for each person (vertex on a graph) governs the evolution of an individual's state between 0 and 1 with regard to alcohol dependence, with 1 indicating absolute certainty of alcohol dependence. The simulations are dependent on initial conditions, the structure of the network, and the resilience distribution of the network. The simulations incorporate multiple realizations of social networks, showing the effect of different network structures. The model suggests that the prevalence of alcohol abuse can be minimized by treating a relatively small percentage of the study population. In the small populations that we studied, the critical point was 10% or less of the study population, but we emphasize that this is within the limitations and assumptions of this model. The use of a simple model that incorporates the influence of the social network neighbors in structured populations shows promise for helping to inform treatment and prevention policy.
Objectivism in information utilization: theory and measurement.
Leary, M R; Shepperd, J A; McNeil, M S; Jenkins, T B; Barnes, B D
1986-01-01
A self-report scale was constructed and validated that measures individual differences in objectivism--the tendency to base one's judgments and beliefs on empirical information and rational considerations. Validity data showed that, compared to people who score low on the Objectivism Scale, highly objective individuals enjoy thinking more, rely more on observable facts when making decisions, and place less emphasis on subjective and intuitive styles of decision making. Among graduate students in psychology, objectivism correlated positively with ratings of research-oriented careers, but negatively with ratings of mental health careers; also, highly objective students were more critical of nonobjective psychological assessment techniques and placed greater importance on research. Objectivism also predicted preferences for newspaper articles, college course selections, and the criteria respondents use when making decisions.
Information processing by biochemical networks: a dynamic approach
Bowsher, Clive G.
2011-01-01
Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on informational grounds. In the context of signalling networks with multiple inputs, the approach identifies the routes and species involved in sequential information processing between input and output modules. An algorithm is developed which allows automated identification of decompositions for large networks and visualization using a tree that encodes the conditional independences. Only stoichiometric information is used and neither simulations nor knowledge of rate parameters are required. A bespoke version of the algorithm for signalling networks identifies the routes of sequential encoding between inputs and outputs, visualized as paths in the tree. Application to the toll-like receptor signalling network reveals that inputs can be informative in ways unanticipated by steady-state analyses, that the information processing structure is not well described as a bow tie, and that encoding for the interferon response is unusually sparse compared with other outputs of this innate immune system. PMID:20685691
Applications of Information Theory for Ecohydrology Model Diagnostics
NASA Astrophysics Data System (ADS)
Ruddell, B. L.; Drewry, D.
2013-12-01
Earth System Models are becoming more complicated and complex as detailed formulations of physical and biological processes operating at multiple scales are integrated together to simulate the connections and feedbacks of the whole system. A prime example of this increase in process fidelity is the terrestrial land surface, where meteorological and hydrological processes drive and interact with the biological functioning of vegetation, together controlling carbon, water, and energy fluxes with the atmosphere. Ecohydrological models that capture these couplings and feedbacks may intentionally or unintentionally create self-organizing or "emergent" dynamics that do not exist when a single model component is used in isolation. It is therefore critical that model diagnostics begin to directly inspect model output for its fidelity to emergent system-scale patterns including observed couplings, feedbacks, thresholds, and controls. Information Theory provides a general class of methods that are able to directly measure coupling, control, and feedback. We apply these methods to compare observations and model results in the context of the Midwest US agro-ecosystem. We utilize a state-of-the-art ecohydrological model, MLCan, which has been extensively validated against eddy covariance observations of carbon, water and energy exchange collected at the Bondville, Illinois FLUXNET site. Using a dynamical process network approach in which system couplings are resolved as directional information flows, we show that MLCan does well at reproducing observed system-scale couplings, feedbacks, thresholds, and controls. We identify important exceptions that point to necessary model improvements. By applying these methods in addition to the standard residual error analysis, it is possible to move beyond asking whether an Earth System Model gets the "right answers", and to instead examine whether the model captures the emergent system-scale structures necessary to be correct for the
A proposed concept for a crustal dynamics information management network
NASA Technical Reports Server (NTRS)
Lohman, G. M.; Renfrow, J. T.
1980-01-01
The findings of a requirements and feasibility analysis of the present and potential producers, users, and repositories of space-derived geodetic information are summarized. A proposed concept is presented for a crustal dynamics information management network that would apply state of the art concepts of information management technology to meet the expanding needs of the producers, users, and archivists of this geodetic information.
Spreading dynamics of an e-commerce preferential information model on scale-free networks
NASA Astrophysics Data System (ADS)
Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding
2017-02-01
In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.
Mutual information after a local quench in conformal field theory
NASA Astrophysics Data System (ADS)
Asplund, Curtis T.; Bernamonti, Alice
2014-03-01
We compute the entanglement entropy and mutual information for two disjoint intervals in two-dimensional conformal field theories as a function of time after a local quench, using the replica trick and boundary conformal field theory. We obtain explicit formulas for the universal contributions, which are leading in the regimes of, for example, close or well-separated intervals of fixed length. The results are largely consistent with the quasiparticle picture, in which entanglement above that present in the ground state is carried by pairs of entangled freely propagating excitations. We also calculate the mutual information for two disjoint intervals in a proposed holographic local quench, whose holographic energy-momentum tensor matches the conformal field theory one. We find that the holographic mutual information shows qualitative differences from the conformal field theory results and we discuss possible interpretations of this.
The g-theorem and quantum information theory
NASA Astrophysics Data System (ADS)
Casini, Horacio; Landea, Ignacio Salazar; Torroba, Gonzalo
2016-10-01
We study boundary renormalization group flows between boundary conformal field theories in 1 + 1 dimensions using methods of quantum information theory. We define an entropic g-function for theories with impurities in terms of the relative entanglement entropy, and we prove that this g-function decreases along boundary renormalization group flows. This entropic g-theorem is valid at zero temperature, and is independent from the g-theorem based on the thermal partition function. We also discuss the mutual information in boundary RG flows, and how it encodes the correlations between the impurity and bulk degrees of freedom. Our results provide a quantum-information understanding of (boundary) RG flow as increase of distinguishability between the UV fixed point and the theory along the RG flow.
On information flow in relay networks
NASA Astrophysics Data System (ADS)
El Gamal, A.
Preliminary investigations conducted by El Gamal and Cover (1980) have shown that a max-flow min-cut interpretation for the capacity expressions of the classes of degraded and semideterministic relay channels can be found. In this paper it is shown that such an interpretation can also be found for fairly general classes of discrete memoryless relay networks. Cover and El Gamal (1979) have obtained general lower and upper bounds to capacity. However, the capacity of the general relay channel is not known. Past results are here extended to establish the capacity of deterministic relay networks with no interference and degraded relay networks. A general upper bound is given to the capacity of any relay network with this upper bound being a natural generalization of Theorem 4 in the study conducted by Cover and El Gamal (1979).
Complex Dynamics in Information Sharing Networks
NASA Astrophysics Data System (ADS)
Cronin, Bruce
This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.
Animal Social Network Theory Can Help Wildlife Conservation.
Snijders, Lysanne; Blumstein, Daniel T; Stanley, Christina R; Franks, Daniel W
2017-08-01
Many animals preferentially associate with certain other individuals. This social structuring can influence how populations respond to changes to their environment, thus making network analysis a promising technique for understanding, predicting, and potentially manipulating population dynamics. Various network statistics can correlate with individual fitness components and key population-level processes, yet the logical role and formal application of animal social network theory for conservation and management have not been well articulated. We outline how understanding of direct and indirect relationships between animals can be profitably applied by wildlife managers and conservationists. By doing so, we aim to stimulate the development and implementation of practical tools for wildlife conservation and management and to inspire novel behavioral research in this field. Copyright © 2017 Elsevier Ltd. All rights reserved.
Modelling mechanical characteristics of microbial biofilms by network theory
Ehret, Alexander E.; Böl, Markus
2013-01-01
In this contribution, we present a constitutive model to describe the mechanical behaviour of microbial biofilms based on classical approaches in the continuum theory of polymer networks. Although the model is particularly developed for the well-studied biofilms formed by mucoid Pseudomonas aeruginosa strains, it could easily be adapted to other biofilms. The basic assumption behind the model is that the network of extracellular polymeric substances can be described as a superposition of worm-like chain networks, each connected by transient junctions of a certain lifetime. Several models that were applied to biofilms previously are included in the presented approach as special cases, and for small shear strains, the governing equations are those of four parallel Maxwell elements. Rheological data given in the literature are very adequately captured by the proposed model, and the simulated response for a series of compression tests at large strains is in good qualitative agreement with reported experimental behaviour. PMID:23034354
Topological analysis of metabolic networks based on petri net theory.
Zevedei-Oancea, Ionela; Schuster, Stefan
2011-01-01
Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.
An Information-Related Systems Theory of Counseling
ERIC Educational Resources Information Center
Wilkinson, Melvin
1973-01-01
The author suggests that a systems theory of counseling should have an adequate theoretical foundation in the way various types of systems handle information. Using the works of W. R. Ashby as a basis, the paper is a beginning attempt to establish this foundation. It describes how information can be used to maintain homeostasis and protect key…
An Information-Related Systems Theory of Counseling
ERIC Educational Resources Information Center
Wilkinson, Melvin
1973-01-01
The author suggests that a systems theory of counseling should have an adequate theoretical foundation in the way various types of systems handle information. Using the works of W. R. Ashby as a basis, the paper is a beginning attempt to establish this foundation. It describes how information can be used to maintain homeostasis and protect key…
Information channel capacity in the field theory estimation
NASA Astrophysics Data System (ADS)
Sładkowski, J.; Syska, J.
2012-12-01
The construction of the information capacity for the vector position parameter in the Minkowskian space-time is presented. This lays the statistical foundations of the kinematical term of the Lagrangian of the physical action for many field theory models, derived by the extremal physical information method of Frieden and Soffer.
Year 7 Students, Information Literacy, and Transfer: A Grounded Theory
ERIC Educational Resources Information Center
Herring, James E.
2011-01-01
This study examined the views of year 7 students, teacher librarians, and teachers in three state secondary schools in rural New South Wales, Australia, on information literacy and transfer. The aims of the study included the development of a grounded theory in relation to information literacy and transfer in these schools. The study's perspective…
NASA Astrophysics Data System (ADS)
Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan
2016-11-01
Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.
Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan
2016-11-01
Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6-7% of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.
Actor-network theory: a tool to support ethical analysis of commercial genetic testing.
Williams-Jones, Bryn; Graham, Janice E
2003-12-01
Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies.
Gest, Scott D; Osgood, D Wayne; Feinberg, Mark E; Bierman, Karen L; Moody, James
2011-12-01
A majority of school-based prevention programs target the modification of setting-level social dynamics, either explicitly (e.g., by changing schools' organizational, cultural or instructional systems that influence children's relationships), or implicitly (e.g., by altering behavioral norms designed to influence children's social affiliations and interactions). Yet, in outcome analyses of these programs, the rich and complicated set of peer network dynamics is often reduced to an aggregation of individual characteristics or assessed with methods that do not account for the interdependencies of network data. In this paper, we present concepts and analytic methods from the field of social network analysis and illustrate their great value to prevention science--both as a source of tools for refining program theories and as methods that enable more sophisticated and focused tests of intervention effects. An additional goal is to inform discussions of the broader implications of social network analysis for public health efforts.
A quantitative approach to measure road network information based on edge diversity
NASA Astrophysics Data System (ADS)
Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing
2015-12-01
The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.
NASA Astrophysics Data System (ADS)
Ninio, Anat
2014-12-01
In the target article [1] Cong and Liu provide a clear and informative introduction to the use of complex networks in research studying language. I would like to add the perspective of a researcher of language acquisition who has been hopeful that network theory illuminates processes of development [2,3], but feels a certain difficulty with studies applying network analysis to the development of syntax.
Network theory to understand microarray studies of complex diseases.
Benson, Mikael; Breitling, Rainer
2006-09-01
Complex diseases, such as allergy, diabetes and obesity depend on altered interactions between multiple genes, rather than changes in a single causal gene. DNA microarray studies of a complex disease often implicate hundreds of genes in the pathogenesis. This indicates that many different mechanisms and pathways are involved. How can we understand such complexity? How can hypotheses be formulated and tested? One approach is to organize the data in network models and to analyze these in a top-down manner. Globally, networks in nature are often characterized by a small number of highly connected nodes, while the majority of nodes have few connections. The highly connected nodes serve as hubs that affect many other nodes. Such hubs have key roles in the network. In yeast cells, for example, deletion of highly connected proteins is associated with increased lethality, compared to deletion of less connected proteins. This suggests the biological relevance of networks. Moving down in the network structure, there may be sub-networks or modules with specific functions. These modules may be further dissected to analyze individual nodes. In the context of DNA microarray studies of complex diseases, gene-interaction networks may contain modules of co-regulated or interacting genes that have distinct biological functions. Such modules may be linked to specific gene polymorphisms, transcription factors, cellular functions and disease mechanisms. Genes that are reliably active only in the context of their modules can be considered markers for the activity of the modules and may thus be promising candidates for biomarkers or therapeutic targets. This review aims to give an introduction to network theory and how it can be applied to microarray studies of complex diseases.
Minimal model of a heat engine: information theory approach.
Zhou, Yun; Segal, Dvira
2010-07-01
We construct a generic model for a heat engine using information theory concepts, attributing irreversible energy dissipation to the information transmission channels. Using several forms for the channel capacity, classical and quantum, we demonstrate that our model recovers both the Carnot principle in the reversible limit, and the universal maximum power efficiency expression of nonreversible thermodynamics in the linear response regime. We expect the model to be very useful as a testbed for studying fundamental topics in thermodynamics, and for providing new insights into the relationship between information theory and actual thermal devices.
An integration of integrated information theory with fundamental physics
Barrett, Adam B.
2014-01-01
To truly eliminate Cartesian ghosts from the science of consciousness, we must describe consciousness as an aspect of the physical. Integrated Information Theory states that consciousness arises from intrinsic information generated by dynamical systems; however existing formulations of this theory are not applicable to standard models of fundamental physical entities. Modern physics has shown that fields are fundamental entities, and in particular that the electromagnetic field is fundamental. Here I hypothesize that consciousness arises from information intrinsic to fundamental fields. This hypothesis unites fundamental physics with what we know empirically about the neuroscience underlying consciousness, and it bypasses the need to consider quantum effects. PMID:24550877
An integration of integrated information theory with fundamental physics.
Barrett, Adam B
2014-01-01
To truly eliminate Cartesian ghosts from the science of consciousness, we must describe consciousness as an aspect of the physical. Integrated Information Theory states that consciousness arises from intrinsic information generated by dynamical systems; however existing formulations of this theory are not applicable to standard models of fundamental physical entities. Modern physics has shown that fields are fundamental entities, and in particular that the electromagnetic field is fundamental. Here I hypothesize that consciousness arises from information intrinsic to fundamental fields. This hypothesis unites fundamental physics with what we know empirically about the neuroscience underlying consciousness, and it bypasses the need to consider quantum effects.
Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.
Ding, Rui; Ujang, Norsidah; Hamid, Hussain Bin; Wu, Jianjun
2015-01-01
Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.
Towards a Semiotic Information Position Framework for Network Centric Warfare
2011-06-01
Centric Warfare Topics Information and Knowledge Exploration Information and Knowledge Exploitation Concepts, Theory , and Policy Saša...Transformation Roadmaps are based on the NCW theory [45], and NEC is at the core of 16th ICCRTS: Collective C2 in Multinational Civil-Military Operations...situational awareness, has long been a key aspect of military theory . For instance, Clausewitz talked about the “fog of war” [12, p. 104] and Sun Tzu wrote
Tensor Networks for Lattice Gauge Theories with Continuous Groups
NASA Astrophysics Data System (ADS)
Tagliacozzo, L.; Celi, A.; Lewenstein, M.
2014-10-01
We discuss how to formulate lattice gauge theories in the tensor-network language. In this way, we obtain both a consistent-truncation scheme of the Kogut-Susskind lattice gauge theories and a tensor-network variational ansatz for gauge-invariant states that can be used in actual numerical computations. Our construction is also applied to the simplest realization of the quantum link models or gauge magnets and provides a clear way to understand their microscopic relation with the Kogut-Susskind lattice gauge theories. We also introduce a new set of gauge-invariant operators that modify continuously Rokhsar-Kivelson wave functions and can be used to extend the phase diagrams of known models. As an example, we characterize the transition between the deconfined phase of the Z2 lattice gauge theory and the Rokhsar-Kivelson point of the U (1 ) gauge magnet in 2D in terms of entanglement entropy. The topological entropy serves as an order parameter for the transition but not the Schmidt gap.
Implications of information theory in optical fibre communications.
Agrell, Erik; Alvarado, Alex; Kschischang, Frank R
2016-03-06
Recent decades have witnessed steady improvements in our ability to harness the information-carrying capability of optical fibres. Will this process continue, or will progress eventually stall? Information theory predicts that all channels have a limited capacity depending on the available transmission resources, and thus it is inevitable that the pace of improvements will slow. However, information theory also provides insights into how transmission resources should, in principle, best be exploited, and thus may serve as a guide for where to look for better ways to squeeze more out of a precious resource. This tutorial paper reviews the basic concepts of information theory and their application in fibre-optic communications. © 2016 The Author(s).
Game theory for Wireless Sensor Networks: a survey.
Shi, Hai-Yan; Wang, Wan-Liang; Kwok, Ngai-Ming; Chen, Sheng-Yong
2012-01-01
Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field.
Game Theory for Wireless Sensor Networks: A Survey
Shi, Hai-Yan; Wang, Wan-Liang; Kwok, Ngai-Ming; Chen, Sheng-Yong
2012-01-01
Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field. PMID:23012533
Affinity based information diffusion model in social networks
NASA Astrophysics Data System (ADS)
Liu, Hongli; Xie, Yun; Hu, Haibo; Chen, Zhigao
2014-12-01
There is a widespread intuitive sense that people prefer participating in spreading the information in which they are interested. The affinity of people with information disseminated can affect the information propagation in social networks. In this paper, we propose an information diffusion model incorporating the mechanism of affinity of people with information which considers the fitness of affinity values of people with affinity threshold of the information. We find that the final size of information diffusion is affected by affinity threshold of the information, average degree of the network and the probability of people's losing their interest in the information. We also explore the effects of other factors on information spreading by numerical simulations and find that the probabilities of people's questioning and confirming the information can affect the propagation speed, but not the final scope.
Processing information system for highly specialized information in corporate networks
NASA Astrophysics Data System (ADS)
Petrosyan, M. O.; Kovalev, I. V.; Zelenkov, P. V.; Brezitskaya, VV; Prohorovich, G. A.
2016-11-01
The new structure for formation system and management system for highly specialized information in corporate systems is offered. The main distinguishing feature of this structure is that it involves the processing of multilingual information in a single user request.
ERIC Educational Resources Information Center
Kerr, Paulette A.
2010-01-01
This research was conducted to investigate the relationships between conceptions and practice of information literacy in academic libraries. To create a structure for the investigation, the research adopted the framework of Argyris and Schon (1974) in which professional practice is examined via theories of action, namely espoused theories and…
The Case of Mandy: Applying Holland's Theory and Cognitive Information Processing Theory.
ERIC Educational Resources Information Center
Reardon, Robert C.; Wright, Laura K.
1999-01-01
Discusses the application of Holland's theory and cognitive information processing theory to the case of a college student who was deciding about a major and a future career. The outcome of the student's case, her personal reactions, and practical implications, are discussed. (Author/GCP)
Mining Heterogeneous Social Networks for Egocentric Information Abstraction
NASA Astrophysics Data System (ADS)
Li, Cheng-Te; Lin, Shou-De
Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify combination of relations as features and compute statistical dependencies as feature values. These features, either linear or eyelie, intend to capture the semantic information in the surrounding environment of the ego. Then we design three abstraction measures to distill representative and important information to construct the abstracted graphs for visual presentation. The evaluations conducted on a real world movie datasct and an artificial crime dataset demonstrate that the abstractions can indeed retain significant information and facilitate more accurate and efficient human analysis.
Tensegrity II. How structural networks influence cellular information processing networks
NASA Technical Reports Server (NTRS)
Ingber, Donald E.
2003-01-01
The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.
Improving gene regulatory network inference using network topology information.
Nair, Ajay; Chetty, Madhu; Wangikar, Pramod P
2015-09-01
Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.
Tensegrity II. How structural networks influence cellular information processing networks
NASA Technical Reports Server (NTRS)
Ingber, Donald E.
2003-01-01
The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.
1990-06-01
yielding information useful in mathematics and in computing. Resource-aware logics restrict the number of times an assumption may be used in a proof and...family of quasi-structures any cut-reduction reduces the total number of formula-occurrences in the proof-network. As indicated in section (1.1.2), the...problems in practice, say, in number theory or in combinatorics - at least not without additional extensive work on the practical conditions of their
Networked electronic information services at the Austin Hospital, Melbourne
NASA Astrophysics Data System (ADS)
O'Keefe, Graeme J.; Egan, Gary F.; O'Callaghan, Daniel; McKay, W.; Hennessy, Oliver; Morrison, Iain
1994-05-01
A wide area computer network has been installed at the Austin Hospital, Melbourne. The network consists of optic fiber segments between buildings, an ethernet spine through the main buildings and ethernet segments throughout each department. The network is connected to Internet via an ISDN link to the University of Melbourne computer network. The Austin hospital network is used for intra-hospital image distribution, external image distribution, internal and external electronic mail via Internet, electronic information access, file transfer via Internet, and remote login to Internet networked computers. Present and future developments include secure patient record access for internal users, confidential information transmission using public key encryption techniques, external dial-up connectivity for teleradiology, and research and development into medical image processing and analysis.
Medical libraries, bioinformatics, and networked information: a coming convergence?
Lynch, C
1999-01-01
Libraries will be changed by technological and social developments that are fueled by information technology, bioinformatics, and networked information. Libraries in highly focused settings such as the health sciences are at a pivotal point in their development as the synthesis of historically diverse and independent information sources transforms health care institutions. Boundaries are breaking down between published literature and research data, between research databases and clinical patient data, and between consumer health information and professional literature. This paper focuses on the dynamics that are occurring with networked information sources and the roles that libraries will need to play in the world of medical informatics in the early twenty-first century. PMID:10550026
Medical libraries, bioinformatics, and networked information: a coming convergence?
Lynch, C
1999-10-01
Libraries will be changed by technological and social developments that are fueled by information technology, bioinformatics, and networked information. Libraries in highly focused settings such as the health sciences are at a pivotal point in their development as the synthesis of historically diverse and independent information sources transforms health care institutions. Boundaries are breaking down between published literature and research data, between research databases and clinical patient data, and between consumer health information and professional literature. This paper focuses on the dynamics that are occurring with networked information sources and the roles that libraries will need to play in the world of medical informatics in the early twenty-first century.
Library and Information Networks: Centralization and Decentralization.
ERIC Educational Resources Information Center
Segal, JoAnn S.
1988-01-01
Describes the development of centralized library networks and the current factors that make library sharing on a smaller scale feasible. The discussion covers the need to decide the level at which library cooperation should occur and the possibility of linking via the Open System Interface Reference Model. (37 references) (CLB)
Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks
Baptista, Murilo S.; de Carvalho, Josué X.; Hussein, Mahir S.
2008-01-01
This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons. PMID:18941516
Complexity measurement based on information theory and kolmogorov complexity.
Lui, Leong Ting; Terrazas, Germán; Zenil, Hector; Alexander, Cameron; Krasnogor, Natalio
2015-01-01
In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.
A neural network theory of proportional analogy-making.
Jani, N G; Levine, D S
2000-03-01
A neural network model that can simulate the learning of some simple proportional analogies is presented. These analogies include, for example, (a) red-square:red-circle :: yellow-square:?, (b) apple:red :: banana: ?, (c) a:b :: c:?. Underlying the development of this network is a theory for how the brain learns the nature of association between pairs of concepts. Traditional Hebbian learning of associations is necessary for this process but not sufficient. This is because it simply says, for example, that the concepts "apple" and "red" have been associated, but says nothing about the nature of this relationship. The types of context-dependent interlevel connections in the network suggest a semilocal type of learning that in some manner involves association among more than two nodes or neurons at once. Such connections have been called synaptic triads, and related to potential cell responses in the prefrontal cortex. Some additional types of connections are suggested by the problem of modeling analogies. These types of connections have not yet been verified by brain imaging, but the work herein suggests that they may occur and, possibly, be made and broken quickly in the course of working memory encoding. These working memory connections are referred to as differential, delayed and anti-Hebbian connections. In these connections, one can learn transitions such as "keep red the same"; "change red to yellow"; "turn off red"; "turn on yellow," and so forth. Also, included in the network is a kind of weight transport so that, for example, red to red can be transported to a different instance of color, such as yellow to yellow. The network instantiation developed here, based on common connectionist building blocks such as associative learning, competition, and adaptive resonance, along with additional principles suggested by analogy data, is a step toward a theory of interactions among several brain areas to develop and learn meaningful relationships between concepts.
Incorporating profile information in community detection for online social networks
NASA Astrophysics Data System (ADS)
Fan, W.; Yeung, K. H.
2014-07-01
Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.
Preface of the Symposium 15: Cryptology, Information Security, and Networks
NASA Astrophysics Data System (ADS)
Caballero-Gil, Pino; Meletiou, Gerasimos C.
2007-12-01
This session of the International Conference of Computational Methods in Sciences and Engineering (ICCMSE) aims at bringing together scientists in the fields of cryptography, data security, information assurance and networks in general, in order to build connections between them and at the same time to promote research and open new directions on the different aspects of network security. Theoretical research papers as well as results on practical applications in areas related to network security are going to be presented.
Advanced information processing system: Input/output network management software
NASA Technical Reports Server (NTRS)
Nagle, Gail; Alger, Linda; Kemp, Alexander
1988-01-01
The purpose of this document is to provide the software requirements and specifications for the Input/Output Network Management Services for the Advanced Information Processing System. This introduction and overview section is provided to briefly outline the overall architecture and software requirements of the AIPS system before discussing the details of the design requirements and specifications of the AIPS I/O Network Management software. A brief overview of the AIPS architecture followed by a more detailed description of the network architecture.
Informal payment for health care and the theory of 'INXIT'.
Gaal, Peter; McKee, Martin
2004-01-01
Informal payments are known to be widespread in the post-communist health care systems of Central and Eastern Europe. However, their role and nature remains contentious, with the debate characterized by much polemic. This paper steps back from this debate to examine the theoretical basis for understanding the persistence of informal payments. The authors develop a cognitive behavioural model of informal payment, which draws on the theory of government failure and extends Hirschman's theory of 'exit, voice, loyalty', the behavioural responses to 'decline in firms, organizations and states'. It is argued that informal payment represents another possible behavioural reaction: 'inxit', which becomes important when the channels of exit and voice are blocked. The theory is applied to explain informal payments in Hungary, but can be shown to be relevant to other countries facing similar issues. The paper examines the proposed policies to tackle informal payments, and on the basis of the theory of 'inxit' it advocates that solutions should contain an appropriate balance between exit and voice to optimize the chances of maintaining a good standard of public services.
Information Security Analysis Using Game Theory and Simulation
Schlicher, Bob G; Abercrombie, Robert K
2012-01-01
Information security analysis can be performed using game theory implemented in dynamic simulations of Agent Based Models (ABMs). Such simulations can be verified with the results from game theory analysis and further used to explore larger scale, real world scenarios involving multiple attackers, defenders, and information assets. Our approach addresses imperfect information and scalability that allows us to also address previous limitations of current stochastic game models. Such models only consider perfect information assuming that the defender is always able to detect attacks; assuming that the state transition probabilities are fixed before the game assuming that the players actions are always synchronous; and that most models are not scalable with the size and complexity of systems under consideration. Our use of ABMs yields results of selected experiments that demonstrate our proposed approach and provides a quantitative measure for realistic information systems and their related security scenarios.
Information Systems and Networks for Technology Transfer. Final Report.
ERIC Educational Resources Information Center
Page, John; Szentivanyi, Tibor
Results of a survey of the information resources available in industrialized countries which might be used in a United Nations technology transfer program for developing countries are presented. Information systems and networks, organized information collections of a scientific and technical character, and the machinery used to disseminate this…
Grower Communication Networks: Information Sources for Organic Farmers
ERIC Educational Resources Information Center
Crawford, Chelsi; Grossman, Julie; Warren, Sarah T.; Cubbage, Fred
2015-01-01
This article reports on a study to determine which information sources organic growers use to inform farming practices by conducting in-depth semi-structured interviews with 23 organic farmers across 17 North Carolina counties. Effective information sources included: networking, agricultural organizations, universities, conferences, Extension, Web…
Information Systems and Networks for Technology Transfer. Final Report.
ERIC Educational Resources Information Center
Page, John; Szentivanyi, Tibor
Results of a survey of the information resources available in industrialized countries which might be used in a United Nations technology transfer program for developing countries are presented. Information systems and networks, organized information collections of a scientific and technical character, and the machinery used to disseminate this…
Grower Communication Networks: Information Sources for Organic Farmers
ERIC Educational Resources Information Center
Crawford, Chelsi; Grossman, Julie; Warren, Sarah T.; Cubbage, Fred
2015-01-01
This article reports on a study to determine which information sources organic growers use to inform farming practices by conducting in-depth semi-structured interviews with 23 organic farmers across 17 North Carolina counties. Effective information sources included: networking, agricultural organizations, universities, conferences, Extension, Web…
Algebraic Information Theory and Stochastic Resonance for Binary-Input Binary-Output Channels
2012-03-01
Transactions on Neural Networks, 19(12):71–89, 2009. [15] Claude E. Shannon . A mathematical theory of communication. Bell Systems Technical Journal, 27:379...Thus, a (2,2) channel is uniquely identified with a point in the unit square [0, 1]× [0, 1]. Using standard Shannon information theory [15] we have...ND . (7) which is now thresholded resulting in Y Y = { o1, if R ≤ θ; o2 if R > θ. (8) II-A. Shannon Model of the (0, 1|2 ; θ;N (µ,σ2)) threshold
Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics
NASA Technical Reports Server (NTRS)
Wolpert, David H.
2005-01-01
A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all red-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principle formulation of bounded rationality and a set of new types of mean field theory in statistical physics; it also shows that those topics are fundamentally one and the same.
Information Networks: A Probablistic Model for Hierarchical Message Transfer.
ERIC Educational Resources Information Center
Bhat, U. Narayan; And Others
A strictly hierarchical message transfer scheme requires that a message follow a specified referral path unless finally it is either rejected or filled at any one of the information centers of the network. Thus at each node in the network three decisions can be made: satisfy, reject or refer the message to the succeeding node in the hierarchy.…
Libraries in the Global, National, and Local Networked Information Infrastructure.
ERIC Educational Resources Information Center
McClure, Charles R.
This paper explores the challenges and opportunities facing libraries as they evolve into the electronic networked environment, and looks at options for libraries in the year 2000 and beyond. The internationally networked environment has fundamentally changed the way in which people acquire and use information resources and services. The paper…
Emerging Communities: Integrating Networked Information into Library Services (Book Review).
ERIC Educational Resources Information Center
Afifi, Marianne
1995-01-01
Reviews this collection of papers, edited by Ann P. Bishop, which present the current state of networking as it relates to libraries and the community. Recommends the book as a compendium of lessons, learned and to be learned, as networked information becomes an integral and necessary part of the library world. (JMV)
Are Social Networking Websites Educational? Information Capsule. Volume 0909
ERIC Educational Resources Information Center
Blazer, Christie
2009-01-01
More and more school districts across the country are joining social networking sites, such as Facebook and MySpace. This Information Capsule discusses the frequency with which school districts are using social networking sites, how districts are using the sites, and potential drawbacks associated with their use. Issues for districts to consider…